{
 "cells": [
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   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "executionInfo": {
     "elapsed": 874,
     "status": "ok",
     "timestamp": 1696350310039,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "_IruQKIWR2ns"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.preprocessing import LabelEncoder, LabelBinarizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Conv1D\n",
    "from keras.layers import MaxPooling1D\n",
    "from keras.layers import Activation\n",
    "from keras.layers import Flatten\n",
    "from keras.layers import Dense\n",
    "from keras.optimizers import Adam\n",
    "from keras.layers import Dropout\n",
    "from sklearn.metrics import classification_report, roc_curve, auc, roc_auc_score\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 587,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(35237, 151)\n"
     ]
    },
    {
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       "      <th>146</th>\n",
       "      <th>147</th>\n",
       "      <th>148</th>\n",
       "      <th>149</th>\n",
       "      <th>Labels</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>p01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 151 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4         5         6  \\\n",
       "0  0.490672  0.422251  0.228106 -0.033556 -0.257353 -0.358802 -0.335725   \n",
       "1  0.509611  0.395481  0.137210 -0.151927 -0.330324 -0.348803 -0.269921   \n",
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       "\n",
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       "\n",
       "        145       146       147       148       149  Labels  \n",
       "0  0.018924  0.118015  0.250026  0.403574  0.505076     p01  \n",
       "1 -0.036065  0.056388  0.193431  0.367346  0.499260     p01  \n",
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       "\n",
       "[3 rows x 151 columns]"
      ]
     },
     "execution_count": 587,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename1 = 'ecgid-90-update.csv'\n",
    "filename2 = 'mimiciii_combined_98_subj.csv'\n",
    "filename3 = 'nsrdb-18-subj.csv'\n",
    "filename4 = 'arr-27-subj.csv'\n",
    "\n",
    "df1 = pd.read_csv(filename1)\n",
    "df2 = pd.read_csv(filename2)\n",
    "df3 = pd.read_csv(filename3)\n",
    "df4 = pd.read_csv(filename4)\n",
    "\n",
    "df = pd.concat([df1, df2, df3, df4], ignore_index=True)\n",
    "\n",
    "#convert number-type to string-type for column Labels\n",
    "df['Labels'] = df['Labels'].astype(str)\n",
    "#save the combined df to csv file\n",
    "df.to_csv('C:/Users/aimedic//PycharmProjects/ecg_authentication/mix1-v3.csv', index=False)\n",
    "print(df.shape)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 487,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(16139, 151)\n"
     ]
    },
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       "      <td>3999053</td>\n",
       "    </tr>\n",
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       "</table>\n",
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       "</div>"
      ],
      "text/plain": [
       "              0         1         2         3         4         5         6  \\\n",
       "16136  0.477865  0.434068  0.389884  0.316964  0.244045  0.171174  0.098998   \n",
       "16137  0.484166  0.457217  0.429750  0.364027  0.298035  0.222255  0.146949   \n",
       "16138  0.486111  0.442218  0.397900  0.322103  0.246271  0.169156  0.092745   \n",
       "\n",
       "              7         8         9  ...       141       142       143  \\\n",
       "16136  0.043509 -0.010829 -0.044727  ... -0.084585 -0.049042 -0.012162   \n",
       "16137  0.083032  0.020232 -0.022721  ... -0.104790 -0.082006 -0.058125   \n",
       "16138  0.033225 -0.025082 -0.061843  ... -0.084733 -0.053285 -0.020612   \n",
       "\n",
       "            144       145       146       147       148       149   Labels  \n",
       "16136  0.052212  0.117731  0.200421  0.283582  0.355717  0.427703  3999053  \n",
       "16137 -0.008318  0.042570  0.115588  0.189212  0.263952  0.338707  3999053  \n",
       "16138  0.037292  0.096247  0.171482  0.247162  0.314192  0.381105  3999053  \n",
       "\n",
       "[3 rows x 151 columns]"
      ]
     },
     "execution_count": 487,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#filename = 'arr-27-subj.csv'\n",
    "#filename = 'nsrdb-18-subj.csv'\n",
    "#filename = 'ecgid-90-update.csv'\n",
    "filename = 'mimiciii_combined_98_subj.csv'\n",
    "# filename = 'mix-215-subj.csv'\n",
    "#filename = 'mixed2-199.csv'\n",
    "#filename = 'mix1-172-update.csv'\n",
    "# filename = 'mixed-215.csv'\n",
    "df = pd.read_csv(filename)\n",
    "#convert number-type to string-type for column Labels\n",
    "df['Labels'] = df['Labels'].astype(str)\n",
    "\n",
    "print(df.shape)\n",
    "df.tail(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 652,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 740
    },
    "executionInfo": {
     "elapsed": 2928,
     "status": "ok",
     "timestamp": 1696350312964,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "TNoHdWFGRKwq",
    "outputId": "4af15303-8043-4b7f-8acd-3d43a1c4eea8",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each label\n",
    "label_counts = df['Labels'].value_counts()\n",
    "min_samples_count = label_counts.min()\n",
    "mean_samples_count = label_counts.mean()\n",
    "max_samples_count = label_counts.max()\n",
    "# exit()\n",
    "# Plot the labels numbers\n",
    "plt.figure(figsize=(18, 10))\n",
    "plt.rcParams['font.size']=20\n",
    "label_counts.plot(kind='bar', )\n",
    "plt.xlabel('Subject ID', fontsize =20)\n",
    "plt.ylabel('Number of Samples (# ECG Beats)', fontsize =20)\n",
    "# plt.title('Initial Sample Distribution of 172 Subjects from ECG-ID & MIMIC-III Databases', fontsize =25, fontweight='bold')\n",
    "plt.title('Initial Sample Distribution of '+f'{filename}', fontsize =25, fontweight='bold')\n",
    "plt.xticks(range(len(label_counts)), rotation=90)\n",
    "plt.yticks(np.arange(0, max(label_counts)+60,50))\n",
    "# Add a line indicating the average\n",
    "plt.axhline(min_samples_count, color='green', linestyle='--', label=f'Min: {min_samples_count:.2f}', linewidth = 2.0)\n",
    "plt.axhline(mean_samples_count, color='red', linestyle='--', label=f'Mean: {mean_samples_count:.2f}', linewidth = 2.0)\n",
    "plt.axhline(max_samples_count, color='purple', linestyle='--', label=f'Max: {max_samples_count:.2f}', linewidth = 2.0)\n",
    "plt.legend()\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 653,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.utils import resample\n",
    "\n",
    "# Set the desired number of samples for each label\n",
    "#desired_samples = int(mean_samples_count)\n",
    "desired_samples = int(max_samples_count)\n",
    "#desired_samples = 500\n",
    "# Calculate the number of samples in each label\n",
    "label_counts = df['Labels'].value_counts()\n",
    "\n",
    "# Identify labels with more samples than desired\n",
    "labels_to_downsample = label_counts[label_counts > desired_samples].index\n",
    "\n",
    "# Identify labels with fewer samples than desired\n",
    "labels_to_upsample = label_counts[label_counts <= desired_samples].index\n",
    "\n",
    "# Create an empty DataFrame to store the balanced dataset\n",
    "balanced_df = pd.DataFrame()\n",
    "\n",
    "# Downsample labels with more samples than desired\n",
    "for label in labels_to_downsample:\n",
    "    label_data = df[df['Labels'] == label]\n",
    "    downsampled_data = resample(label_data, replace=False, n_samples=desired_samples, random_state=42)\n",
    "    balanced_df = pd.concat([balanced_df, downsampled_data])\n",
    "\n",
    "# Upsample labels with fewer samples than desired\n",
    "for label in labels_to_upsample:\n",
    "    label_data = df[df['Labels'] == label]\n",
    "    upsampled_data = resample(label_data, replace=True, n_samples=desired_samples, random_state=42)\n",
    "    balanced_df = pd.concat([balanced_df, upsampled_data])\n",
    "\n",
    "# Shuffle the balanced DataFrame to mix the samples\n",
    "balanced_df = balanced_df.sample(frac=1, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 654,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Labels\n",
      "3955576    718\n",
      "p01        718\n",
      "121        718\n",
      "102        718\n",
      "p61        718\n",
      "          ... \n",
      "p21        718\n",
      "3784577    718\n",
      "p49        718\n",
      "3007104    718\n",
      "p51        718\n",
      "Name: count, Length: 233, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(balanced_df['Labels'].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 655,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 654,
     "status": "ok",
     "timestamp": 1696350315485,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "S36JVRDSTJjK",
    "outputId": "fda098ef-7a4f-414e-cd6e-f57995940e07"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(167294, 150)\n",
      "(167294,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[3.01558564e-01, 2.61075668e-01, 2.13927153e-01, ...,\n",
       "        1.75461010e-01, 2.28043958e-01, 2.77884251e-01],\n",
       "       [1.51312442e+02, 1.20241225e+02, 7.66600017e+01, ...,\n",
       "        6.27860158e+01, 1.00148820e+02, 1.38575171e+02],\n",
       "       [1.93219204e-01, 1.68431825e-01, 1.29046626e-01, ...,\n",
       "        9.96630280e-02, 1.41266268e-01, 1.79926430e-01],\n",
       "       ...,\n",
       "       [3.02047030e-01, 2.54771591e-01, 1.44392393e-01, ...,\n",
       "        1.28924809e-01, 2.21814359e-01, 2.83907698e-01],\n",
       "       [4.36302230e-01, 3.81911382e-01, 2.58696519e-01, ...,\n",
       "        2.16264903e-01, 3.52472775e-01, 4.32663210e-01],\n",
       "       [9.09937998e-01, 8.13012053e-01, 5.83144526e-01, ...,\n",
       "        4.92573789e-01, 7.07881856e-01, 8.34124378e-01]])"
      ]
     },
     "execution_count": 655,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data=df.iloc[:, 0:-1].values\n",
    "# label=df.iloc[:,-1].values\n",
    "# divide the data and the label\n",
    "# for data, take all data from 1st col except the last col\n",
    "# for label, take data only from the last col\n",
    "data=balanced_df.iloc[:, 0:-1].values\n",
    "label=balanced_df.iloc[:,-1].values\n",
    "print(data.shape)\n",
    "print(label.shape)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 656,
   "metadata": {},
   "outputs": [],
   "source": [
    "# from collections import Counter\n",
    "# from imblearn.combine import SMOTEENN\n",
    "# # Create a SMOTEENN object\n",
    "# smote_enn = SMOTEENN(sampling_strategy='auto', random_state=42)\n",
    "# # Fit and resample the dataset\n",
    "# X_resampled, y_resampled = smote_enn.fit_resample(data, label)\n",
    "# # print('before resampling: ', Counter(label))\n",
    "# # print('after resampling: ', Counter(y_resampled))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 657,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Sso1Gert27eT9Y+v633XOb9sfawBk/46fvl67RERERERERERERPT65LlrR+/evV9pRkII7Ny5E0+fPn0rAm8AcPbsWTm4VrFiRcTFxWHChAlYvnw5YmNjAQCmpqZo2LAhvv76azRq1EhrPZcuXZKn/fz8dM7P2NgY3t7eOHfuHC5fvmyw5aB3w/Tp09F92Lg33YxchYaGom/fvgCAwZ+PwZCRY1+5zmfPnuGf/bsBAD5+5eBXvuIr15lXDo5OqFS1Os6eOoHjx49j+fLlqNI0SCPf7du3MWXKFADZQ8w2bPqBRp4Fs6bh19nTAQDf/jgPlUZ+opGn18BPMWnUMDx79gyjR4+We9cqRd+6iZCQEACAt7e31uCbm5sbunfvjpUrV+LIoX3YE7YVzVurt3n3ji04cmgfAKBNxy5wc3PLy+ogIiIiIiIiIiIiokKW5+DbsmXLCjyTLVu2YOLEiXj69Kn8npeXV4HrMxTVoFlWVhaqV6+Oa9euqeVJS0vD3r17sW/fPoSEhGDMmDEa9cTExAAArKysYG9vr3eeXl5eOHfuHB4/fozU1FSYmZm9+oIQveXWrl2L9LQ0AEDbTvnr9Xbn5g1EnDgm/3/a0RIJCQkAgKTERLWeYacdLdGyZUuNQNSwMRMwKLg9MjMz0bdvX7Tu2BkftGkPV/eiSEqIx7bQ0/jpp5/kc1T7Lj1QorRPgZa13UfB2LJ+Fc6c+Bfz5s3DgwcP0LR9MGzt7HHh7CksmjMTcXFxUCgU+Pnnn3UOvzllyhTs2LkLz58+wdhP++PiuQh8HNwJALBjxw78+OOPAAAHJ2d8OvrrArWViIiIiIiIiIiIiAyvUH90aefOnZg4cSJOnz4NILv3m7u7O7766isMGDCgMGedJ8+ePZOnp0+fjpSUFLRs2RLffvstKlWqhLi4OGzcuBFjx47FixcvMHbsWPj5+SEoSL0HSnx8PADA2to613laWVnJ0wkJCVqDb6mpqUhNTZX/j4uLy/eyEb1NVqxYASB7+M3WHT7KV9mIE8cw4YuhWtNinz/TSDtw4IBG8K1GnfpYsWIFBg4ciMTEROzYuA47Nq7TWmfLdh3x9ZSZ+WqjKiMjI/y0eDVG9Q/GiRMnsHHjRmzcuFEtj5mZGebOnYtWrVrhXEys1nq8vLzw87I1+Lx/Dzx59BDL5s/Bsvlz1PI4u7hi9uJVcHUvWuD2EhEREREREREREZFhFcqPTu3Zswd169ZF27Ztcfr0aQghUKRIEfz444+4fv06hg4dClNT08KYdb4kJibK0ykpKWjevDl27NiBGjVqwMzMDEWKFMHgwYOxY8cO+feXxo0bpzFspnLoyrwsk2qwLTk5WWuekJAQ2NnZya+3oZcgUUFdu3YN//77LwCgdoNGcHZxfSPt6NatGyIjIzF+/HhUrlYDdvYOMDY2hoWlFXx9fdG7d2/s378f0+ctgckrnp8cHJ1w5MgRzJ8/H/Xr14e9gyPMzMzhWawEPuzWG6dOnUL//v1zradSlerYsCccA4Z/CW/fsrC2toa1tTUqVqyIAcO/xMa9R1CpSvVXaisRERERERERERERGZZBe74dOnQI48ePR3h4OIDsnm5OTk4YNWoUPv30U1haWhpydq/M3Nxc7f/p06fDyMhII1/9+vXRsWNHbNiwAZcvX8b58+dRqVIljXrS/j+snj6qPdosLCy05hk3bhxGjhwp/x8XF8cA3DugUaNGEELo7MmUm6DO3RDUuVue0kt42mukL/ljBwCgkpY0VZMmTcKkSZN0trNPnz6o2qx9XpqcJz4+PgZdL6rLl7PO3Jbd09MT3377LToNHKn2vr46VQ0ZOTbPv4FnbGyMIUOGYMiQIRp1ls+lnaocHJ3w6aiv8emor/PcTiIiIiIiIiIiIiJ6cwzS8+3IkSNo1qwZmjRpgvDwcAghYGdnh2+//RY3b97E6NGj37rAGwDY2NjI00WKFEGVKlV05m3RooU8feLECa31KH+HSh/V3na6hqk0MzODra2t2ouIiIiIiIiIiIiIiIjefq8UfDt58iRatWqFBg0a4MCBAxBCwNraGuPHj8fNmzfxzTff5Ol30N4U1d5knp6eec77+PFjtTRl2cTERMTGxuqtJzo6GkB2sE/b770RERERERERERERERHRu6tAwbezZ88iKCgItWrVwu7duyGEgKWlJcaMGYObN29i8uTJsLOzM3RbDa58+fLydGZmpt68qunGxuqjdZYrV06ejoyM1FlHRkYGrl+/DgAoW7ZsvtpKREREREREREREREREb798Bd8uXryITp06oWrVqtixYweEEDA3N8fIkSNx48YNhISEwNHRsbDaanDFixdHsWLFAAC3bt2CEEJnXmXQDACKFi2qlla/fn15+tChQzrrOHnypDzsZL169QrUZiIiIiIiIiIiIiIiInp75Tn4FhwcjMqVK2Pz5s0QQsDU1BTDhw/H9evXMXPmTBQpUqQw21loPvzwQwBAXFwc9u3bpzPfpk2b5GnVYBsANGrUSO7pt3z5cp1BvNDQUHm6Q4cOBW0yERERERERERERERERvaWMc8+Sbd26dfJ0kSJFMHz4cHh5eWH37t0FnnmvXr0KXNZQRowYgQULFiAlJQUjR47E4cOHYWtrq5Zn1apVOHjwIACgdevWar//BkAORH733Xe4fPkyZs6ciVGjRqnlOXr0KJYsWQIACAgIQI0aNQpvoYiIiIiIiIiIiIiIiOiNyHPwDQAkSQIAPHnyBBMmTHilGUuS9MrBt8OHDyMqKkr+/8mTJ/J0VFSUWk8zAOjTp49GHcWKFcO3336L0aNH4/z586hZsybGjBmDSpUqIS4uDps2bcKCBQsAALa2tpg9e7bWtowaNQrr1q3D1atXMXr0aERFRaFr166wsLDAgQMHMHXqVGRkZMDCwgI//fTTKy03ERERERERERERERERvZ3yFXzT95tob8LixYuxfPlyrWnh4eEIDw9Xe09b8A3IDpw9e/YM06dPx5UrV/Dxxx9r5HFxccGWLVvg4+OjtQ4bGxuEhYUhMDAQ165dw6JFi7Bo0SK1PLa2tli9ejX8/f1zXzgiIiIiIiIiIiIiIiJ65+Q5+HbgwIHCbMcbFxISgnbt2mHBggX4559/cP/+fZibm6NMmTJo164dhg0bJv+umy7e3t6IiIjAvHnz8McffyAqKgppaWnw8vJCYGAgPvvsMxQvXvw1LRERERERERERERERERG9bnkOvgUEBBRmOwokNDRUY2jJV1GnTh3UqVPnleqwsrLC6NGjMXr0aAO1ioiIiIiIiIiIiIiIiN4VijfdACIiIiIiIiIiIiIiIqL/CgbfiIiIiIiIiIiIiIiIiAyEwTciIiIiIiIiIiIiIiIiA2HwjYiIiIiIiIiIiIiIiMhAGHwjIiIiIiIiIiIiIiIiMhAG34iIiIiIiIiIiIiIiIgMhME3IiIiIiIiIiIiIiIiIgNh8I2IiIiIiIiIiIiIiIjIQBh8IyIiIiIiIiIiIiIiIjIQBt+IiIiIiIiIiIiIiIiIDMT4TTeA6L/i4MGDaNy4sfy/pZU19kdcAWCvt1xycjLc3NwQFxcnv7d4/XZU+qhNIbX0v6VVnUq4FxOdrzI7j5xFUa9iau9lZGTg/Pnz2PTXQVw8G4ELZ07hxrUryMzMBADcvHkTMLbPU/1ZWVlYt24d1qxZg1OnTuHR48dQSAo4FimCCpWrYcQnA9CqVat8tVmfwwf2YOKm33HixAk8fvwY9o7OKF+5Cjp17436jZvnqY6MjAysX7kUOzf/gVvXryEpMRFFi3qgWbNmGD58OGBX1GDtJSIiIiIiIiIiIvovY/CNqJAkJSbgwJ9hqOUzQG++rVu3qgXeqHDZ2dnBuYiLxvtTpkzBpEmTXrn+uNhYfNavG04fP6qRdi/6Du5F38HuHZvx4YcfYvXq1a80r6ysLHw7ZgQ2r12p9v6jB/fw6ME9HPgrDB2De+GPVcugUOju6PzkyRMEBgbixIkTau/fuHEDixYtwvLlyzH2ux/QMbjXK7WXiIiIiIiIiIiI6H3A4BtRITAzM0dqagp2bFqHscP0B99WrlypVobyZ8HqTUhPSwMA+LrZyu9fefAyoPnv4UOYMfkrAEDnzp1hZm6uUY8QQp42MzOHb/mKeP70CaJv38xXe0YP7ScH3kqWLIlRo0bBwrUEMjIycPncGSxbMAfPnz3Fxo0b4ezsjE++mZav+lX98sN3cuCtSpUqGD16NEqXLo39x88h9NefEXnhHDatWYFvShTF1KlTtdaRmZmJDh06yIG3pq3a4sPgXrC1d8CzW5fw/fff49GjR/hu7OdwcXPPc086IiIiIiIiIiIiovcVg29EhSCgeSvs3rEZx/45iAcPHsDNzU1rvkePHmH37t0AgEYftMJf2ze/xlb+N5Qo5S1PV/C0l6ez7GPl6UVzZsrTvXpp771Vp04d/Prrr7AvXhY+fuVgbGyM8Z9/kq/g28WzETj6934AQKlSpXDmzBnY2NjgXEx2W2rWbYAP2rZH15YNERsbi99++w1dBn8BJ+cieZ6H0q0bUVixcC4AoHylKggPD4eFhQUAwMzdB40+aIV+ndrg4rkIzJgxAx9//DG8vb016lm+fDkOHz4MAOjSqx++mvJyXVVq2xStWrVCtWrVEBcXh+kTxqL2gcYwNuZHBxEREREREREREZEuuschI6ICqxPQGM4ursjMzMSaNWt05luzZg0yMjLg5uaG2g0b68xHBRcf9wKH9uwCkB0Qq1+/vtZ8LVq0wKBBg1C2QqUCB5fOnDouT48YMQI2NjYaedyLeqFv374AsoeNPB9xskDzWr14ATIyMgAAY7+bLgfelCwsLDH2u+kAsn/Pbfbs2VrrmTkzO9jm6OiIz7/5ViPd29sb48aNAwDcuXUD+//cUaD2EhEREREREREREb0vGHwjKgRGCiO0bPchgJfDSmqzYsUKAEC3bt1gpDDKc/0HDhxA7969UapUKdTy8UDdssXwYbO6mPX9eNy7d09v2QsXLuD777/H4O4fonmN8qhe2hW1fT3h4+OD3r1749ixY3rLL5g1DZW9HFDZywEAkJKSghkzZqBq1aqo4+eFOn5e6NamKdaELpKDQ2/S7h1b5OE8dfV6M5SM/w9/CWQH+nQpXbr0yzLp6fmejxACB3ZnBxRLepdBpao1tOarVLUGSpT2AZD924KqQ2sC2b3nLl++DCB7OE4LC0ut9fTp00eeZvCNiIiIiIiIiIiISD8G34gKSZsPuwAAIiIicPHiRY3061cjcfr0aQBAz54981RnakoKxgzthyZNmmDFihW4efMmUlKSkZgQj6grl7F84VyUKVMG27dv11r+4MGDqFixIsaPH4+jf+/Howf3kJ6WhuSkRERFRWHFihWoU6cO5kybnKf2PH38CHXq1MHo0aMRERGBpMQEJCUm4OLZ05g2fgw6duyIrKwsrWVv3boFSZIgSRL6fdQmT/MriO0b1gIAJEnK83ouqOKlXw7reOPGDZ35rl+//rJMKc2hIHNz8+ZNPH54HwBQrXZdvXmV6Xfv3sWtW7fU0iKOvwy0BgQE6KzDzc1NbueZk//mu71ERERERERERERE7xMG34gKSdkKlVC6jB8A7b3fdmxaBwCoUKEC/P39c61PCIEvBvfGn9s2AQDatm2LlStXYvnmP7Fy626MnjwN7kU9kZiYiE6dOuHkSc3hDDMyMmBlZYXOnTvjm5BZWLJ+B9buOoT5Kzfgxx9/RPHixQEAS+f9hC3rVufappEDe+LSpUsYPnw49uzZg7U7D2LaL7+hlI8vAGD79u347bffcq2nsMTcuS0Hi+rXr6+3N5oh1AtoiqLFstfhnDlzkJiYqJHn4f27CA0Nldvk41cu3/O5dOmSPF2ydBm9eVXTlb3clG5cuyJP+/n55VJPdg+6B/fuIilJc7mIiIiIiIiIiIiIKNs7HXx79OgRduzYgQkTJqBVq1ZwdnaWe9KoDpOmT1JSEjZt2oQhQ4agRo0acHBwgImJCZycnFCnTh1MmjQJDx48yHObkpKS8MMPP6BGjRpwdHSElZUV/Pz88MUXX+D27dsFXFJ6V7Xt1BUA8Pvvv6sN+SeEwM7NfwDI+1CIm9aswD/7dsPYxAS7du3Ctm3b0KNHD/hXr4VKVWug+8eDsP7Pf1C+fHmkpaVhxIgRGnX4+/sjJiYG69atw0c9+qJ6nXooW6ES6jVqipEjR+Lq1ato3rw5AGDhT9ORmZmpt00Xzkbgr7/+wpw5c9CsWTOUrVgZrdp3QujGXXAq4gIAmD9/fp6WrzDs2LhWXu+FPeQkAJiYmmLaL7/BwdEJ169fR+XKlbFw4UJEnDiGE0cPY/nCuega2BjPnz9HqVKlsHTp0gLNJyYmRp52dffQm9fNo6g8HR0drZb28P5dedrT01NvPa7/r0cIgUf39Q9tSkRERERERERERPQ+M37TDXgVrq6ur1T+3LlzqFevHhISEjTSnj17hmPHjuHYsWOYPXs2Fi1ahC5duuitLyoqCoGBgbh27Zra+1euXMGVK1ewePFirF69Gm3aFN4Qe/R2CWz/EX6e9i2io6Nx8OBBOPlUAQCcOHoYD+7dhUKhQLdu3XKtRwiBZfPnAAC69R2Eli1bas1na2+PGTNmIDAwEOHh4bh98zqKl3z5+2LOzs5652NqaooZM2bA398f92KiceXieZSr5K8zf3CfgWjUqJHG+3YODgjq3A1L5/2E8+fP48WLF7kuY2HYsTG7d6G5uQU6d+78WuZZqWoNrPvzEHavD8WcOXMwePBgtXRLK2t89913+OSTT+Do6IhzMbH5nkd8fPzL+iyt9Oa1sHz5O245z3VJiS//t7a2BlJS8lRPkpYefURERERERERERESU7Z0OvqkqVqwY/Pz8sHv37jyXiYuLkx9G16tXD23atEH16tXh5OSEx48fY9OmTfjtt98QFxeH7t27w9bWFq1atdJaV3x8PFq3bi0H3gYMGICuXbvCwsICBw4cQEhICOLi4tClSxeEh4fnaZjB18150Tw4/zYfMJLk9/wyhXomIwmoWhXYtk3t7RJ9g4HL5/WXA4CRI4HOH8tvKxLiAc8KuZcDYLFoFZJVA0E7dgD/D2zoK1fG3BJXDx7Hm+Dq7oHGjRtj3759WLlyJUZ8mx18UwaFmjRpgqJFi+qrAkD278NF374JAGjeup3evA0bNpSnz506oRZ8yyktNRVPnzxCUmIiFLHWAKDWQ+/KpQt6g2+tO3ykM61cRX+5vps3b0LhXEItvUSJEvK8ChKAys2Zk//K66zRB61ga2tr8HloI4TAn1s3YcP69UhPT9dIT0pMwOrVq+Hh4YGPP/5YSw25S1EJkhmbmurNa2JqJk8nJyerpaWmpsrTpqamAHQH30xV6klJSdaZj4iIiIiIiIiIiOh9904H3yZMmIAaNWqgRo0acHV1xa1bt1CyZMk8l1coFOjcuTMmTpyIcuU0f3fpgw8+QKtWrdChQwdkZmZi2LBhuHbtGiRJ0sg7Y8YMXL16FQDwww8/YNSoUXJanTp10KhRIwQEBCApKQkjRozAwYMH87/AhcwoIR6mD9SHk9P6WN/LS7Pss6fA3ZdD2OkMB8TFqf8vRN7KAZDS09TfSE6Wy+orZ2Jtoye18PXq1Qv79u3Dxo0bMXjcFADA3p3b5LS8uHTujDzdM+gD9MzjvJ88fqjxXmJiIn7++WeErlyN61cj9Q4tGfv8qd76S3j76EyztXeQp+Pj42Gnv9OdwW3fuFaebtcp+LXMMysrC2OG9sPuHVsAAP369cPQoUORYeOOzKxMXLl4HqELfsbBPbvQr18/nDt3Dh9/OSnf8zE3N5enM9LS9OQE0tNeBtgsLCzU0szMXgbU0nKpJ02lHnNzCz05iYiIiIiIiIiIiN5v73TwbfLkya9Uvm7duqhbt67ePEFBQejYsSM2btyI69evIyIiAlWrVlXLk56ejp9//hkAULZsWXzxxRda59WvXz8sXLgQhw4dwokTJ1CjRo1Xar+hZVrbIM3NA6YqvcbScvQoMzWSgCJFNMs6OgEqPbi0lgOAnL2PJClv5QAIkxwhNgsLuay+cunmlniTOnbsiCFDhiAuLg4Hdu8EhEBiQjwsLK3QsWPHPNXx7OnjAs07JUdPp1u3bqFJkya4efNmnsqn6hmGEAAsLHSvW4Xi5U9K5vbbcYaWmpqK3du3AACKuLihdsPGr2W+61cskQNvkyZNwsSJEwG87NlXpUZtVKlRGzO/Go6VK1dizpw5KFW5Fho1196jVhcbm5cB5aQk/UNAJiclydPW1tZqaZZWL//P7gWs+yNBtR5LK/1DXRIRERERERERERG9z97p4Nvr0rhxY2zcuBEAcP36dY3g24EDB+TftOrdu7da0EFVnz59sHDhQgDA5s2b37rg25OBQ/Fk4FBU8rSX34vMMRygapqqW8vW5L2cSlqWtQ0QE5Oncsk5hyZs00Yuq6/c1UIY0jA/rK2t0aFDB6xevRphm9bJQy02adkaVnkMYqgGr35etgaNq78cqvPKA/XehL5uLwOcjzPM1NJ69uyJmzdvQpIkBHXujpbtOqKUTxk4ODqjWikXSJKErKwsGBkZAVAfgvJdsn37dsS9iAUAtGrfSV6ewrZp7UoAgJW1DcaOHasz39SpU7FyZXbeLetW5zv45unpKU8/vH9PT07gwb2XPUu9cvRadXV/GfiOiYnRGBpU1cP/1yNJElzcPfLTXCIiIiIiIiIiIqL3CoNveaD6u0jaHuIfPnxYng4ICNBZT/Xq1WFpaYmkpCSEh4cbtpH0VuvVqxdWr16No38fkN9r+2HXPJe3d3CUp21s7VChwsvgW5Z9rFreCiqBR9XfUrsZdVXeV7/66it0HvylWjnlcKrPnj3Lc7veVitWrJCn23XK+3p+VTejsoeeLeXjqzakY06enp5wdXXFw4cPcev6tXzPR3WY3JvXr+pvk0p62bJlEa+SVsrHV56OjIxEufol9NST3U43j6KwtGTPNyIiIiIiIiIiIiJdtHfRIjWHDh2Sp8uWLauRfunSJXnaz89PZz3Gxsbw9vYGAFy+fNmALaS3XdOmTeHu7o6MjAxkZGSgiKs7atXXHajNya98JXn6zMl/C9SG61cj5ekuXbrozHfy5MkC1f+2ePz4Mf78808AgG/5ivApW/61zdvIKPv7DJkZGbnmTU9P/3+Z/PfKK1myJIq4ugMATh07ojfv6X+PAgCKFi2KEiVKqKVVqVlbnlY9z+X04MED3L4RBQDwr14r3+0lIiIiIiIiIiIiep8w+JaLs2fPIiwsDABQsWJFrcG3mP8PfWhlZQV7e3u99SmHfXv8+LFajzr6bzMyMkLPnj1hamYGUzMztPmws87hSbUpW7EyXP8/1N/G1cuRkstvsWmToRIQSkzU/Tthv/76a77rfpusWbNGDmy16xT8Wudd1KsYACDq6mXExsbqzHfhwgW5h2FRr+L5no8kSWj8QfZQlTejruLc6RNa8507fULujRcUFCT3blQqUcpbPqetX78eyclJGnUAQGhoqDzdpGWbfLeXiIiIiIiIiIiI6H3C4Jseqamp6N+/v/x7W1OmTNGaLz4+eyA3a2vrXOtU/Y2vhIQEnfONi4tTe9G7b/r06TgR9QAnoh5gxLhJ+SqrUCjQ/9ORAICYO7fQq1cvvcHbuLg4zJ07V+294iVLy9OqwRRVCxYswNatW/PVtoK6desWJEmCJEno95HhAjrKISeNjY3Rqn0ng9WbFwHNWgIA0lJTMXLkSK2/mZeakoLhw4fL/zds1kIjz8GDByFJEip7OWD8559onVf3/kPkXnPTxo9BcnKyWnpKcjKmjR8DIHtdjBgxQms9X36ZPfzos2fPMHvKRI3069evIyQkBABQrEQpBt+IiIiIiIiIiIiIcsHffNPj008/lYfg6927N9q2bas1n7IXkqmpaa51qv4OVM6H5UohISGYPHlyfptL/3Ef9fwYR/85iP1/7sAff/yB06dPY9CgQXAqUQ7WtrZIjI/DzevXMO3scWzbtg3m5ubY376HXN6vQiVUqFABFy5cwMKFC3Hz7iO0+bAziri44eH9e/juz83YsGED6tWr987+JuGlS5dw6tQpAEDdRk3h5Fwkz2UTEhKwYcMGRD972fvrzu0b8vSGDRuQrLCU//ctXxGVPBuo1dFz4FBsXrcKz548xrJly3Dt2jUMHjwYRg5FkZmZiciL5/D7skW48f8hQMuWLYugj7oVaFlLlPJG78HDsHTeT7h4LgL16tXDmDFjULp0aew/fg7LFsxB5IVzAIBRo0bBx8dHaz29e/fG0qVLER4ejnXLF+Pp40foGNwLtnb2+HvLZXz33XeIi4uDQqHAmG+nwdiYHxtERERERERERERE+vApqg4hISFYvHgxAKBGjRqYN2+ezrzm5uYAgLS0tFzrVe2tZGFhoTXPuHHjMHLkSPn/uLg4ebhKen9JkoQf5i/FDxPH4o9Vy3D9+nWMHj1aZ34XFxeN8itXrkSTJk3w/Plz7N6xGbt3bFbLU7FiRfzxxx/w8PAolGUobMpebwDQ9sOu+Sr75MkT9O3bV2f6qFGj1P4f/PkYdG6hHnxzcHTCglUbMXJgT9y9cxuHDx/G4cOHtdbn7++PLVu24IVR7kF7XYaNHo9nT55gy7pViIiIQNeumsvcoWtPfP/99zrrMDIywpYtWxAYGIgTJ05g785t2Ltzm1oeMzMzjP3uB9Rv3LzAbSUiIiIiIiIiIiJ6X3DYSS0WLlyIr776CgDg5+eHnTt3qg0XmZONjQ0A3cNIqlL9rS1dw1SamZnB1tZW7UUEACYmJvh66o84e/Yshg0bhooVK8LG1hZGRkawsbWFb/mK6NevHzZs2IDLly9rlPf398eZM2cwePBgeHh6wdjEBHb2DqjgXw0zZ87E8ePH4e7u/gaW7NVlZWVh9erVAAB7e3s0at7qjbTDr3xFbNgTjnnz5uGDDz6Am5sbTExNYWpmBjePomjUvBVWrlyJ48ePo3jx/P/emyqFQoHJM3/B3OXrEBQUBA8PD5iamqKIqzsafxCIeSvWY9KMn3P9fUFnZ2ccOXIEX0+ZiSo1asPewRFmZuYoVaoUBgwYgFOnTqFjcK9XaisRERERERERERHR+4I933JYs2YNPvkk+zeWihcvjj179sDZ2VlvGU9PT/z7779ITExEbGws7O3tdeaNjo4GABQpUkRtCEp69zVq1AhCCJyLiS1Q+aDO3RDUOW9DEFasWBE///wzAGjMr5Knvcp/mkObFitWDAsWLMCQr3WXOxv9XOe8h4wciyEjx+baxhp16qv95lnOdpYoUUJOL+g6U6VQKOTjqyB1Ktujb33mtU5LSyt88skn8rlE/zbSlN99qUGTDzC0V+d8t1OVsbExOvfqh869+mltpyG2EREREREREREREdH7gD3fVGzbtg29evVCVlYW3N3dsW/fPnh6euZarly5cvJ0ZGSkznwZGRm4fv06gOzfeiIiIiIiIiIiIiIiIqL/Fgbf/m/fvn3o3LkzMjIy4OTkhD179qB06dJ5Klu/fn15+tChQzrznTx5Uh52sl69eq/WYCIiIiIiIiIiIiIiInrrMPgG4MiRIwgKCkJqairs7Ozw119/oXz58nku36hRI9jZ2QEAli9frjbUnqrQ0FB5ukOHDq/UZiIiIiIiIiIiIiIiInr7vPfBtzNnzqB169ZITEyElZUVwsLCUK1atXzVYWpqiuHDhwMALl++jJkzZ2rkOXr0KJYsWQIACAgIQI0aNV698URERERERERERERERPRWMX7TDXgVhw8fRlRUlPz/kydP5OmoqCi1nmYA0KdPH7X/r1+/jhYtWiA2NhYA8P3338POzg4XLlzQOU8XFxe4uLhovD9q1CisW7cOV69exejRoxEVFYWuXbvCwsICBw4cwNSpU5GRkQELCwv89NNP+V5WIiIiIiIiIiIiIiIievu908G3xYsXY/ny5VrTwsPDER4ervZezuDbP//8g0ePHsn/f/7557nOc+LEiZg0aZLG+zY2NggLC0NgYCCuXbuGRYsWYdGiRWp5bG1tsXr1avj7++c6HyIiIiIiIiIiIiIiInr3vPfDThqSt7c3IiIiMH36dFSvXh329vawtLSEr68vPv/8c5w7dw5t2rR5080kIiIiIiIiIiIiIiKiQvJO93wLDQ3VGFoyP/r06aPRG+5VWVlZYfTo0Rg9erRB6yUiIiIiIiIiIiIiIqK3H3u+ERERERERERERERERERkIg29EREREREREREREREREBsLgGxEREREREREREREREZGBMPhGREREREREREREREREZCAMvhEREREREREREREREREZCINvRERERERERERERERERAbC4BsRERERERERERERERGRgTD4RkRERERERERERERERGQgDL4RERERERERERERERERGQiDb0REREREREREREREREQGwuAbERERERERERERERERkYEw+EZERERERERERERERERkIMZvugFE/xUHDx5E48aN5f8trayxP+IKAHu95ZKTk+Hm5oa4uDj5vcXrt6PSR20KqaVv3t3oO6js5ZCvMh6eXth19JzG+0mJCbh8/iwunDmN6KvnceLECdy6dUtvGV3i4+Ox8rf5OLhnF65fuYz4+DhYmJujePHiaNiwIZp06A4fv3L5arcuGRkZWLx4MRYtXY5b168hKTERRVzdENjyAwwfPhzly5fPUz3Pnz3F70sX4uj+P+XldivqhcYtWqN7v8Gwd3A0SHuJiIiIiIiIiIiIKG8YfCMqJEmJCTjwZxhq+QzQm2/r1q1qgTfSrngpH63vD+sbjJNHD79y/REREQgKCkJ0dLTa+/Hp6bhw4QIuXLiAhQsX4tPR3+DjT0a80ryePHmCwMBAnDhxQu39mDu3sGjRIixfvhxz585FzZad9NZzLuIkPu/fA08ePVRvc+QlXIu8hM1rVmL24lWo5Nn0ldpLRERERERERERERHnH4BtRITAzM0dqagp2bFqHscP0B99WrlypVuZ94OLmjvPnz8v/X3mgHnz0dbMFAISEhOD3338HALTr1FV7ZULIk46OjqhevTqOHDmChISEPLcnNjYWrVq1wsOH2UGsarXq4qOeH8OzWAk4GKViz549mDdvHtLT0zEnZDLci3qhVdCHea5fVWZmJjp06CAH3pq2aosPg3vB1t4B58+cwrK5P+LRo0cYNGgQfgm1Q/3GzbXW8+BeDIb3Dcbzp09gbGyMkSNHok2b7N6SS9dswKrf5uPxowcY/nEwAiJOw9PTs0DtJSIiIiIiIiIiIqL8YfCNqBAENG+F3Ts249g/B/HgwQO4ublpzffo0SPs3r0bANDog1b4a/vm19jKN8fExAQVKlSQ/8+yj1VLr+Bpj8zMTBw8eBAAYGVtgyYttQ/D2ap9J3Tq3gflK1dFmwZVAQAlSpTIV/Bt8eLFcuDtgzbtMWPBMjmtkqc9AgMD0bhxYwQFBQEAfvt5ZoGDb9v+WIPDh7N76n3yyScYNG6KnFaxSjX0D+6IatWqIS4uDtMnjEXtA41hbKx5qv7lh+/x/OkTAEDIL7/hy8F95DS7khVRrqI/Rn/yMZ49eYxvvvkGoaGhBWovEREREREREREREeWP4k03gOi/qE5AYzi7uCIzMxNr1qzRmW/NmjXIyMiAm5sbajdsrDPf+2jv3r24d+8eAKBZYDuYW1hozdepex+0at8JxUqWKvC8jhw5Ik8PHP6l1jzt2rWDX4VKAIDrVyORmBBfoHmtWDQXQHYvvRkzZmike3t7Y9y4cQCAO7duYP+fOzTyPHjwADs3/wEAqBvQFB+0aa+Rp0XbDqgbkD3c5MqVK/HgwYMCtZeIiIiIiIiIiIiI8ofBt/9LS0vD4sWL0aJFC7i7u8PMzAzW1tbw9fVF37591R7O67Nr1y506NABnp6eMDMzg6enJzp06IBdu3YV8hLQ28RIYYSW7bJ7RimHldRmxYoVAIBu3brBSGGUa73XIi9h0ZyZaNGihbyP1fb1RNsG1fDN50Nw7vQJnWV3bv4DkiRBkiR88sknOvPduXMH9SuUQGUvB7RvXAspycm5tqswKNcNALTrFFyo80pLS5OnixYvoTOfV/GS8nR6enq+53PrRhRuXLsCAOjcuTMsLS215uvTp488rS34tm3bNmRlZQEAgjp30zm/dh9lr7esrCxs27Yt3+0lIiIiIiIiIiIiovxj8A3A7du3UbVqVQwYMAC7d+/GgwcPkJaWhsTERFy9ehWhoaGoV68ehg8fDqHy+1KqsrKy0L9/fwQGBmLLli24e/cu0tLScPfuXWzZsgWBgYEYMGCA/MCc/vvafNgFABAREYGLFy9qpF+/GonTp08DAHr27JlrfSeOHkan5vUwb+YU7N69W97HkpMScefWDWzfsBY9gz6Qe03lFNjhI3Trlh2oWbBgAf7e95dGnqysLPTq1QvxL17A2MQEIT8v0uhxtnX976js5YDKXg5YMGtaru0uiPj4eGzZsgUA4OFVDNVq1y2U+Sj5+vrK03dv39KZL/r2TQCAvYMj7B0c8z2fiOPH5OmAgACd+dzc3FCmTBkAwJmT/2qkK4etBIDqtevprEc1LTw8PF9tJSIiIiIiIiIiIqKCee+Db+np6WjdurUcHKlUqRJCQ0Nx9OhR7N69GxMmTICVlRUA4JdffsH06dO11vP1119jyZIlAIAqVapgzZo1OH78ONasWYMqVaoAyP5dqW+++eY1LBW9DcpWqITSZfwAaO/9tmPTOgBAhQoV4O/vn2t9mRkZsLC0wgdtOuDXX3/FwYMHcfr0acxfuQFfjP8eHp5eAIBp06Zh2bJlWuuYP38+ihUrBgCY+OUwPH3yWC39hx9+wKFDhwAAn3wxDmUrVs7bwhrYhg0bkJSUBABo07ELJEkq1Pn1798fRkbZPQ9/++VHrXnCwsIQeeEcAOCjHn0LNB9lrzcA8PPz05tXmf7g3l0kJSWqpV26dAkAYGNrC2cXV511FHF1g7WNDQDg8uXLBWozEREREREREREREeXPex9827p1qxx4q1OnDk6fPo3evXujdu3aaN68OSZPnoxDhw7BxMQEADB9+nRkZGSo1XH16lXMnDkTAFC9enWEh4eja9euqFGjBrp27YrDhw+jevXqAIAZM2YgKirqNS4hvUltO3UFAPz+++9qvSaFEPJvdvXq1StPdfmWr4jdxy9ixoKlGDRoEAICAlClShXUa9QUvQYOxbZDJ1G7Qfbvxk2ePBmZmZkaddjZ2WHlypVQKBR49uQxJo0aJqedOnUKEyZMAABUq1UXfYd8VrCFNgDVISfbfti10OdXtmxZzJs3D0ZGRvhr+2b079IOf27bhAtnTmPXrl344osv0LFjRwDZv7H28aefF2g+D+/flac9PT315vXyyg6mCiHw6P49tbSYmBgAgIubR67zdHUvCgCIjo7OV1uJiIiIiIiIiIiIqGDe++Cb6m+5jRs3Tu79oqpatWpo06YNACA2NlajB8lPP/0kB+R++eUXWOQYps/S0hK//PILACAjIwOzZ8826DLQ2yuw/UdQKBSIjo7GwYMH5fdPHD2MB/fuQqFQyENB5sbB0Qm2dnY6001MTTHym8kAsodSPXPmjNZ8DRs2xJgxYwAAf+/9C+tXLEFychK6d++O9PR02NnZ4fufFkCheDOnh/t3o+Xed3Xr1kWxkqVey3wHDRqEo0ePommrtjhx5B+MGdoP3ds2RWBgIGbNmgUvLy9MmvELfgldC0tLqwLNIykxQZ62trbWm1fZ4za7nHrPt/j4eACApZX+OgDA4v9tTUhIyCUnERERERERERERERmC8ZtuwJuWlpYmT5cqpfshf+nSpbWWEUJg69atALKHiatdu7bW8rVr14avry+uXLmCrVu3Yu7cuYU+lF5+XVt0GlG/RWCv0ct2pWeq/8bdXiMJ7lXdEbwtWO39o323Ye/lJ3rLAUCdkXVg1bnsy3wJaZjlOSvXcgBQbVFrOFR6OcTe1R1XsWPwjlzLCXNjND+Yt95lhubq7oHGjRtj3759WLlyJUZ8mz0E6Y6N2UNONmnSBEWLFi1Q3ampqXj48CGuR92Tf0tQ9ScJz549i+oflNZadvLkydi6YxcunT+DH78bjyOH9uPKlewhEefNmwcPz2I65xvUuRuCOuctYFgQYZvWy70E89or0BDi4uKwdOlS/Hv4oNb0GzduYMemdShR2htVamg/znOTmpoqT5uamurNa2ZmJk+npCSrpaWkpACA3CNXH+V8kpOTc8lJRERERERERERERIbw3gfffH195ekbN26gfPnyWvNdv34dACBJEnx8fOT3b968iXv3soeECwgI0DuvgIAAXLlyBXfv3sWtW7dQsmTJV22+QWUkpCHlQQJS9ORJAWDnpdn7KvVZMuLvxustBwCpcalQ6zMkRJ7KAUBWepZaWnpyus6yquWMrfUHOQpbr169sG/fPmzcuBGDx00BAOzduU1Oy4+kpESsWboQB3dlD5eqbWhJpSdPnuhMMzExQcgvi9ClVSOkJCfhwO6dAIDg4GB0794d52Ji89UuQ9qxaT2A7OBTly5dcOc1dNh68OABmjVrhosXL8LcwhLDx05AizYd4OpRFCXtjPD3339j/PjxOHn0MAZ0DcL3sxegZbuO+Z6PakAtLS0N5ubmOvOqBurMzdV705qbmyMpKQnp6em5zlP5ZYGcPXKJiIiIiIiIiIiIqHC898G34OBgfPPNN4iLi8P06dMRGBioMfRkREQEwsLCAADdunWDra2tnHbp0iV52s/PT++8VNMvX7781gXfjK1NYe5mDRM9Pd9MjCRYFrHUKGvmaAGbojZ6ywGAma2Z2vuQpDyVAwCFifowiCYWJnJZfeWE+ZvdzTt27IghQ4YgLi4uO8glBBIT4mFhaSX/jlhe3I2+gwFd2+Hundt5yp9bT6cSpX3QZ/Aw/Dp7OgCgSJEimD9/fp7bUxjOR5zCzairAIB27drB3t4edxJiC32+w4YNw8WLFyFJEuYuX4caderLaY6O9mjfvj2aNWuGylWr48a1K5j45bDsPJ72+ZqP6jCRCQkJeoNviSpDTVpaqQ9zaWNjg6SkJLVhLHVJTsquJ7dhLomIiIiIiIiIiIjIMN774JuzszNWrlyJ4OBghIeHo0aNGhgxYgTKlCmDhIQEhIeH48cff0RaWhqqVq2KH3/8Ua18TEyMPO3p6al3Xl5eXvJ0dHS0YRfEAHwGVoXPwKqopBJQyNkDqpKOYEOdZe3yXE41zcTaFCNjRua7HACUaVNGLpufcq+btbU1OnTogNWrVyNs0zp5SMUmLVur/a5Xbr7+bBDu3rkNSZLQt29fdO3aFWXLlsWDVBOYmJpCkiRkZWWhSnEnAJDno0tCfBy2bVgj///kyROcPn0aTZo0KcBSGsb2jWvl6dc15GRcbCw2bdoEAGjWrJla4E2VtbU1+g8bia+GD0JKchL+3LYJjauMzde8XN1fDjEaExMDZ2dnnXmV5whJkuDi7qGW5unpiYcPH+LRg3u5zvPh/bsA1M8/RERERERERERERFR43vvgG5Ddw+bUqVP48ccfsWTJEvTu3Vst3dXVFd999x0GDBgAS0v1Xl/x8S+HPcytZ4lqoCUhQXePldTUVLUh5+Li4vK0HPT26tWrF1avXo2jfx+Q32v7Ydc8l78ZdRURJ44BAPp/OhKLfp4ppz1TCS6+iH2e5zpDxo/Gveg7AAAraxskJsSjT58+OHfuXJ7rMKT09HT8tS07CObi4oKWLVu+lvneunFN/s28qlWr6s1brqK/PK3soZcfpXxeDnMbGRkJf39/nXkjIyMBAG4eRWFpqR6kLVeuHE6dOoX4uDg8efQQzi6u2qrA44cPkPD/c1TZsmW15iEiIiIiIiIiIiIiw1LknuW/Ly0tDStWrMDWrVu19hZ6+PAhVq1ahb1792qkpaS8/HUxU1P9vy2m+ntP+oYEDAkJgZ2dnfxij5V3X9OmTeHu7o6MjAxkZGSgiKs7atXX/xuBqq5fjZSnW7TtoDPfpbMRearvjz/+wI6N6wAAHYN7IeSXRQCye1sNGTIkz+0ypH/27Ubs82cAsod3NTZ+Pd8NMDJ6OZ+MjAy9eTNUfmPNqADtq1Kztjx96NAhnfkePHiAq1ezg3v+1WtppNev/7J33slj4TrrUU2rV69evtpKRERERERERERERAXz3gffEhMT0axZM4SEhODZs2cYPXo0Ll++jNTUVLx48QK7d+9G/fr1cfLkSbRv3x6zZs1SK6/6m01paWl656Xam83CwkJnvnHjxuHFixfy620copLyx8jICD179oSpmRlMzczQ5sPOUCjyfvipBoWSk5N05vtj1bJc67p79y4GDRoEAChWsjRGTZqKgGYtMXjwYADA2rVrEbZ5fZ7bZiiqQ07m7H1amDy8ikGSsn8j8J9//tGb9+S/L4NZRb2K53teJUp5y73f1q9fj6Qk7dsyNDRUnm7Sso1Gert27eT9Z+v633XOb9sf2cOKKhQKtGvXLt/tJSIiIiIiIiIiIqL8e++Db5MmTZIfuC9ZsgTTp0+Hn58fTE1NYWtri+bNm+PAgQNo3LgxhBAYNWoUzp49K5e3sbGRp/UNJQlkB/qU9A1RaWZmBltbW7UXvfumT5+OE1EPcCLqAUaMm5SvssVLlpant/6hPdiyfsUSHNi9U289Qgj07t0bz58/h7GxMabOWSgPafjjjz/C1zc7MBTyzSjcv6s96Lt1/e+o7OWAyl4OWDBrWr6WQ5cXz5/jn/27AQA+fuX0DsdoaA6OTqhdO7tH2vHjx+WAVU63b9/G4l+yg++SJKFh0w808iyYNU1eN6oBNFW9Bn4KAHKwP6fr168jJCQEAFCsRCmtwTc3NzcEdvgIAHDk0D7sCduqkWf3ji04cmgfAKBnz55wc3PT2h4iIiIiIiIiIiIiMqz3+jffhBBYunQpAKBMmTI6e9sYGxvju+++Q/369ZGVlYXQ0FDMnj0bAODp6Snni4mJ0Ts/1R5sHEqS8sOvQiV4+5ZF1JXL2LAqFF3SktCzZ0+4u7vj8JkrCNu8HnvCtsK/Ri2cOfGvznpmz56NffuyAzIDPhuFilWqyWmWlpZYtWoV6tati/i4OHwzYgh+W7ctXz30CurPbRuR/v+eo207Beer7J2bN+TfwzvtmP2bjMpAeFJiotwzTJmW/Vty5mp1TJ06Fc2aNUNmZiYmfDEU/4Yfwgdt2sPVvShe3MweIvKnn37C06dPAQDtu/RAidI+BVrWdh8FY8+WtQgPD8e8efNw5WY0Ogb3gq2dPS6cPYWlv/yIuLg4KBQKjPl2ms7hN4eN/gbhB/fh+dMnGPtpfzy+eRlt2mQH6pau2YCVi+YBABycnPH9998XqK1ERERERERERERElH/vdfDt4cOHePYs+zemqlSpojdvtWovgxSRkS9/f6tcuXJa39dGNb1s2bL5aiu93yRJwpQ5CzGgSzvEvYjF+vXrsX69+tCQPn7lMHNBKJpV175vXbt8EV999RUAoE6dOhgw7AuNPNWrV8fEiRPxzTff4OSxcCxf+Av6DvnM8AuUw/b///6ckZERWv+/R1deRZw4hglfDNWaFvv8mUbagQMH4Ojtr/Zeo0aNsGLFCgwcOBCJiYnYsXGd/Jt4ObVs1xFfT5mZrzaqMjIywpYtWxAYGIgTJ05g785t2Ltzm1oeMzMzzJ07FzUbN9dZj5uHJ35etgaf9++BJ48eYvr06Zg+fbpaHmcXV8xevErtSwJEREREREREREREVLje62EnVXuUqP6mljbp6elay5UsWRIeHh4AsnvH6PP3338DAIoWLYoSJUrkt7n0nvMrXxHr//obH/Xoi+LFi8PExASOjo6o4F8NI7/5Dqu370MRV+1DC6alpmLc8IFITU2FtbU1Vq5cCSMjI615x44diyo1sodhnDdzKiIvni+0ZQKAa9eu4XzESQBA7QaN4OziWqjz06Vbt26IjIzEwM9GoXK1GrCzd4CxsTGsrKzg6+uL3r1747d12zB93hKYmJq+0rycnZ1x5MgRzJ8/H1Vq1Ia9gyPMzMzhWawEBgwYgFOnTqF///651lOpSnVs2BOOAcO/RIUKFWBtbQ1ra2v4+JXDgOFfYuPeI6hUpfortZWIiIiIiIiIiIiI8ue97vnm6OgIW1tbxMXF4ejRo8jIyNA5xJtqYK1kyZLytCRJCAoKwoIFCxAZGYljx47Jvx+l6tixY3LPt6CgIEiSZOCloTetUaNGEELgXExsgcoHde6GoM7d9OZxL+qFb0JmoZKnvfxezvkJITTSTM3MsGFPuN5ySkZGRgjdtOuV2pkfPj4+OBv9vMDlVdujb/nysuyenp4Y+uVXGPrlV/kqpzRk5FgMGTlWo5w2xsbGGDJkCOq1VR9mM7dyOTk4OuHTUV9j0ZwZeW4nERERERERERERERWe97rnm0KhQOvWrQEA9+7dw5QpU7Tme/78OcaMGSP/r/xdJaURI0bIvYiGDRuG5ORktfTk5GQMGzYMQPYD9xEjRhhqEYiIiIiIiIiIiIiIiOgt8l4H3wBgwoQJsLS0BABMmjQJ7dq1w8aNGxEREYGjR49i9uzZ8Pf3x6VLlwAATZs2xQcffKBWR5kyZTBq1CgAwMmTJ1GvXj2sW7cOJ0+exLp161CvXj2cPJk9rN6oUaPg4+PzGpeQiIiIiIiIiIiIiIiIXpf3ethJAPDz88PWrVsRHByMJ0+eYPv27di+fbvWvE2aNMEff/yhNW3KlCl49OgRli5dioiICHTt2lUjT79+/fD9998btP1ERERERERERERERET09njve74BQLNmzRAZGYnp06ejUaNGKFKkCExMTGBhYYGSJUuic+fO2LJlC/bu3QsHBwetdSgUCixZsgRhYWEICgqCh4cHTE1N4eHhgaCgIOzcuROLFy+GQsFVTkRERERERERERERE9F/13vd8U3JycsLo0aMxevToV6onMDAQgYGBBmoVERERERERERERERERvUvYDYuIiIiIiIiIiIiIiIjIQBh8IyIiIiIiIiIiIiIiIjIQBt+IiIiIiIiIiIiIiIiIDITBNyIiIiIiIiIiIiIiIiIDYfCNiIiIiIiIiIiIiIiIyEAYfCMiIiIiIiIiIiIiIiIyEAbfiIiIiIiIiIiIiIiIiAyEwTciIiIiIiIiIiIiIiIiA2HwjYiIiIiIiIiIiIiIiMhAGHwjIiIiIiIiIiIiIiIiMhAG34iIiIiIiIiIiIiIiIgMhME3IiIiIiIiIiIiIiIiIgNh8I2IiIiIiIiIiIiIiIjIQBh8IyIiIiIiIiIiIiIiIjIQBt+IiIiIiIiIiIiIiIiIDITBNyIiIiIiIiIiIiIiIiIDMX7TDaC3R7u54W+6CYVq26f13nQT3iuVvRwAABMnTsSkSZPebGOIiIiIiIiIiIiIiF4T9nzTY8yYMZAkSX4dPHgw1zK7du1Chw4d4OnpCTMzM3h6eqJDhw7YtWtX4TeYiIiIiIiIiIiIiIiI3ij2fNPhzJkzmDVrVp7zZ2VlYeDAgViyZIna+3fv3sXdu3exZcsW9O/fHwsXLoRCwZgnERERERERERERERHRfxGjQFooA2kZGRlwcXHJU5mvv/5aDrxVqVIFa9aswfHjx7FmzRpUqVIFALB48WJ88803hdZuIiIiIiIiIiIiIiIierMYfNPi559/xokTJ+Dn54d+/frlmv/q1auYOXMmAKB69eoIDw9H165dUaNGDXTt2hWHDx9G9erVAQAzZsxAVFRUobaf3g4RJ45h0qjh8PX1ha2tLUxNTdG8Rnl82qcL1ob+hrgXL7SWO7hnF74Y1FseutTJyQk9gz7AknmzkZSYoHN+C2ZNQ2UvB0iSpLddBw8ehCRJqOzlgBNHD2ukN2rUCJW9HNDvozYAgIf372HkyJHw9vaGhYUFGlYshcHdP8ThA3u01t+qTiX5994AYPLkyfL8lK/xn3+it41ERERERERERERERO8qDjuZw507dzB+/HgAwK+//ooDBw7kWuann35CRkYGAOCXX36BhYWFWrqlpSV++eUX1KlTBxkZGZg9ezbmzZtn+MbTWyElORmTRg3Drq0bNdIePbiHRw/u4Z99u/H82VPUnzVNTktNScHYYQOw/88damWePXuGZ8+e4dzpE1iz7DfMXb4OlTwbFPpyANkBxM/798DzZ0/l91JSUnD07/04+vd+jPz6W/z4/fjX0hYiIiIiIiIiIiIioncBe77lMHToUCQkJKB3794ICAjINb8QAlu3bgUA+Pn5oXbt2lrz1a5dG76+vgCArVu3QghhuEbTWyMrKwuf9esuB96KlSyN2bNn459//sGpU6fwS+ha9P90JIqVKKVR9pvPP5EDb77lKmDFihU4ceIE/vrrLwR17g5JkvD44X0M7BqEu3fvFvqyPH70EJ/37wFJocC0adNw+PBhHD9+HF9OmAIbOzsAwM/Tv8XFixfVyi1YvQkb9oTL/w8ZMgTnz5/Hhj3h8uvT0Rx+lYiIiIiIiIiIiIj+m9jzTcX69euxY8cOODo6ysNI5ubmzZu4d+8eAOQarAsICMCVK1dw9+5d3Lp1CyVLlnzlNtPbZe7cuTj2T3ZvySYt22D63MWoXtpVTjd2KYWGTVtg6Kiv8fjhA/n9sLAw7N6xGQBQq34A5i1fj2qlXv7eoFu5mqhcrQa+HTMCL2KfY+TIkVi3bl2hLsvtG1Hw8PRC6KY/0bxGOfl9M3cflK9cBR93ao2MjAwsWrQIc+bMkdNLlPJWq8fFxQUVKlRAln1sobaXiIiIiIiIiIiIiOhtwJ5v/xcbG4vPPvsMADB9+nQ4OzvnqdylS5fkaT8/P715VdMvX75cgFbS2ywrKwszZswAALi6e2DKTwtgamamNa9CoYCru4f8v3IYUmMTE3z741yYmJpqlPmwW2/UbtAIALBp0ybcv3/fwEugacy309XaqVS1Zh1UrJL9O4b//PNPobeDiIiIiIiIiIiIiOhdweDb/40ePRoPHjxAvXr10K9fvzyXi4mJkac9PT315vXy8pKno6Oj899IequdOXNG3h86dusNSyvrPJXLyMjAoUOHAAB1GjaGm4fu/ahjcC+5zMGDB1+twbmwsbNDw6YtdKaXrVgZAHDjxo1CbQcRERERERERERER0buEw04iu+fO4sWLYWxsjF9//RWSJOW5bHx8vDxtba0/2GJlZSVPJyQk6MyXmpqK1NRU+f+4uLg8t4fenIiICHm6as06eS5348YNJCUlAQAq+lfXm7dilWry9IULF/LZwvwpVqI0FArd8Xk7ewcA6scAEREREREREREREdH77r3v+ZaWloaBAwdCCIHPP/8cFSpUyFf5lJQUedpUy1CBqsxUhiBMTk7WmS8kJAR2dnbyS7XHHL29njx5Ik8XcXHVk1Pds2fP5GnHXIY7dS7ysl7VcoXBwsJCb7oyMJeVlVWo7SAiIiIiIiIiIiIiepe898G3qVOnIjIyEsWKFcPEiRPzXd7c3FyeTktL05tXtTebvsDGuHHj8OLFC/nFISrfH/npdUlERERERERERERERG+f93rYycjISISEhAAAfvnlF7VhIfPKxsZGntY3lCQAJCYmytP6hqg0MzNT6yVH7wZnlV5rjx89REnvMnkq5+joKE8/ffxYb94njx9qLQcAksoQkVlZWTqHjFTdD4mIiIiIiIiIiIiIyLDe6+Db7NmzkZaWhlKlSiEpKQlr167VyKP6u1r79+/HgwcPAABt27aFlZUVPD095fSYmBi981PtwcahJP97qlatKk+f/vcIatZtkKdypUqVgqWlJZKSknD+zEm9eS+cOS1P5xwi1crqZUD3+fPncHJy0lrH1atX89QuIiIiIiIiIiIiIiLKv/c6+KYcBvLGjRsIDg7ONf93330nT9+8eRNWVlYoV66c/F5kZKTe8qrpZcuWzW9z6S1XuXJleHl5ITo6GpvWrECvgUNhaaW7h6OSsbExAgICsGvXLhz75yAe3r8LV/eiWvNuWrNSLtOoUSO1tKJexeXpkydPokWLFlrr0BZkLgxmZuZITU1RG26ViIiIiIiIiIiIiOi/7r3/zbdXVbJkSXh4eAAADh06pDfv33//DQAoWrQoSpQoUdhNo9dMoVBg1KhRAICH9+/h6xFDkK7jdwCzsrLw6MF9+f+hQ4cCANLT0jDxy+FIT0/XKLN57Soc/Xs/AKBjx45wd3dXS69cvSaMjbPj6bNnz4YQQqOOGTNm4Pjx4wVYuvxzdnUFAFy/fv21zI+IiIiIiIiIiIiI6G3wXgffQkNDIYTQ+5o4caKc/8CBA/L7yuCZJEkICgoCkN2z7dixY1rndezYMbnnW1BQECRJKtyFozdi6NChqN2gMQBg/5878GHzepgzZw7Cw8MRERGBwwf2YP6PIQhqVBMbf18ul2vdujU+aNMeAHD07/3oGdQcq1evxqlTp7B3715MGjUck0cPBwDY2Ttg1qxZGvN2ci6C5q2z98W//voL7dq1Q/iBvbh84RwO/LUTXwzqjdGjR6Nu3bqFvBayVa5WEwCwbds2LFy4ENciL+HOzRu4c/MGnj7R/9t2RERERERERERERETvqvd62ElDGTFiBBYtWoTMzEwMGzYMf//9NywsLOT05ORkDBs2DED2cIEjRox4Qy2lwqZQKPDTklUY//kn2BO2FbdvROV5e38/ewEyMjKw/88duHz+LHr06KGRp4irO+YuX4eiRbUPSzlq4lREXTqHa9euYceOHdixY4daeteuXdG/f380a9Ys38uWX70HDcO+nduQmpqKwYMHq6W16xSMxn/8XuhtICIiIiIiIiIiIiJ63d7rnm+GUqZMGXm4wZMnT6JevXpYt24dTp48iXXr1qFevXo4efIkAGDUqFHw8fF5k82lQmZhYYmZv4bit3Xb0ObDLihZsiQsLCxgamoKN4+iCGjWEuOnzUavgUPVypmZm2P2bysxZ+nvaNqqLTw8PGBqagoHBwdUrFIdw8dOwLZDx+FXvqLOeTsVccG///6LMWPGwMfHB6ZmZrCzd0C1WnUx9eeFWLNmDYyMjAp7FQAA/MpXxNGjRxEcHIxixYrB1MzstcyXiIiIiIiIiIiIiOhNYs83A5kyZQoePXqEpUuXIiIiAl27dtXI069fP3z//fdvoHV5s+3TegCASp728nvnYmLV8vwX0l6XmnUboGbdBvluS6PmrdCoeasCL4ODgwOmTZuGadOmaS3XqFEjCCF01nnw4ME8zW/IyLEYMnKsWjtzqlKlCn7/PbuH25vaDkRERERERERERERErxN7vhmIQqHAkiVLEBYWhqCgILnXkoeHB4KCgrBz504sXrwYCgVXORERERERERERERER0X8Ve77lYtKkSZg0aVKe8wcGBiIwMLDwGkRERERERERERERERERvLXbDIiIiIiIiIiIiIiIiIjIQBt+IiIiIiIiIiIiIiIiIDITBNyIiIiIiIiIiIiIiIiIDYfCNiIiIiIiIiIiIiIiIyEAYfCMiIiIiIiIiIiIiIiIyEAbfiIiIiIiIiIiIiIiIiAyEwTciIiIiIiIiIiIiIiIiA2HwjYiIiIiIiIiIiIiIiMhAGHx7Dwgh3nQTiIjAUxERERERERERERG9Dxh8+w9TKLI3b1ZW1htuCRG9z4TIQpYQSMlk9I2IiIiIiIiIiIj++xh8+w8zMTGBkZEREhMT33RTiOh9lpGGxLRMxKXwiwBERERERERERET038fg23+YJEmwsbFBXFwch54kojcmPTUFFx6mIIOnISIiIiIiIiIiInoPMPj2H2dnZ4f09HRkxj9jAI6IXruslATExiXg9P3UN90UIiIiIiIiIiIiotfC+E03gAqXpaUlPD09cSgiEk6pKTC3soZkbIbk5GRIkgQAEBlpamVSUlLkaaYxjWlMy29acnIyMjIy8OLFCzx78hg7rsThzEP1PERERERERERERET/VQy+vQdsbGwwI/wZqnskombRBNhbGMMs9bmc/uh5slp+02QLpjGNaUx75TQzMzMsPvkMx+6mgIiIiIiIiIiIiOh9weDbe+L2iwzcfpGBzZGJsDNT4OjYxnJa/00H1fLu+6IR05jGNKa9UpqRkRGMjY1xJOYyiIiIiIiIiIiIiN4nDL69Z7IE8DwlC+bm5vJ7d+Mz1fIwjWlMY5qh0oiIiIiIiIiIiIjeN4o33QAiIiIiIiIiIiIiIiKi/woG3wrB7du38cUXX8DPzw9WVlZwdHREjRo1MGPGDCQlJb3p5hEREREREREREREREVEh4bCTBrZ9+3b06NEDcXFx8ntJSUk4efIkTp48icWLFyMsLAze3t5vsJVERERERERERERERERUGNjzzYAiIiLQpUsXxMXFwdraGlOmTMGRI0ewb98+DBgwAABw9epVtG7dGvHx8W+4tURERERERERERERERGRo7PlmQJ999hmSk5NhbGyM3bt3o06dOnJakyZN4OPjg9GjR+Pq1av48ccfMWnSpDfXWCIiIiIiIiIiIiIiIjI49nwzkOPHj+Off/4BAPTr108t8Kb0xRdfoGzZsgCAOXPmID09/bW2kYiIiIiIiIiIiIiIiAoXg28GsmXLFnm6b9++WvMoFAr06tULABAbG4sDBw68jqYRERERERERERERERHRa8Lgm4EcPnwYAGBlZYVq1arpzBcQECBPh4eHF3q7iIiIiIiIiIiIiIiI6PVh8M1ALl++DADw9vaGsbHun9Lz8/PTKENERERERERERERERET/DQy+GUBKSgqePHkCAPD09NSb18HBAVZWVgCA6OjoQm8bERERERERERERERERvT66u2hRnsXHx8vT1tbWuea3srJCYmIiEhIStKanpqYiNTVV/v/FixcAgLi4OGSlJqnljYuLk6eZxjSmMY1przctZzrTmMY0pjGNaUx7v9JypjONaUxjGtOYxrT3Ky1nOtOYxrR3M02ZLoSAoUjCkLW9p6Kjo1GsWDEAQM+ePbFixQq9+YsVK4bo6GiULl0aUVFRGumTJk3C5MmTC6WtREREREREREREREREpC46OjrX0Q3zij3fDMDc3FyeTktLyzW/slebhYWF1vRx48Zh5MiR8v9ZWVl49uwZnJycIEkS4uLi4OXlhejoaNja2qqVZRrTmMY0pjGNaUxjWuGkvW3tYRrTmMY0pjGNaUxjGtOYxjSmMY1pr54mhEB8fDw8PDxgKAy+GYCNjY08rWsoSVWJiYkAdA9RaWZmBjMzM7X37O3tNfLZ2tpq7DRMYxrTmMY0pjGNaUwr3LS3rT1MYxrTmMY0pjGNaUxjGtOYxjSmMe3V0uzs7LTmKSiFQWt7T5mbm8PJyQkAEBMTozfv8+fP5eCbl5dXobeNiIiIiIiIiIiIiIiIXh8G3wykXLlyAICoqChkZGTozBcZGSlPly1bttDbRURERERERERERERERK8Pg28GUr9+fQDZQ0qeOnVKZ75Dhw7J0/Xq1SvQvMzMzDBx4kSNoSmZxjSmMY1pTGMa05hWeGlvW3uYxjSmMY1pTGMa05jGNKYxjWlMY5ph0wxFEkKIQqv9PXL8+HHUqlULADBo0CD8+uuvGnmysrJQoUIFXL58Gfb29nj06BFMTExed1OJiIiIiIiIiIiIiIiokLDnm4HUrFkTDRo0AAAsWbIER48e1cjz448/4vLlywCAzz77jIE3IiIiIiIiIiIiIiKi/xj2fDOgiIgI1KtXD8nJybC2tsZXX32Fxo0bIzk5GWvXrsWiRYsAAGXKlMHJkydhY2PzhltMREREREREREREREREhsTgm4Ft374dPXr0QFxcnNb0MmXKICwsDN7e3q+5ZURERERERERERERERFTYGHwrBLdv38acOXMQFhaGmJgYmJqawtvbGx999BE+/fRTWFpavukmEhERERERERERERERUSFg8I2IiIiIiIiIiIiIiIjIQBRvugFERERERERERERERERE/xXGb7oBRERERESk38WLF7Fx40YAwIQJE95wa4jebenp6UhISEBycjIsLCxgbW0NExOTN90sInpHZGRkID4+HsnJybCxsYGNjc2bbhIRERG9hTjsJL1Rt27dwt9//w0A6NWr1xtuTe6Sk5MRExOjcbPu6ekJCwuLd/YiPCMjA8+ePYOxsTEcHR3zXT639UJ5964dE5R3jx8/hrGxMRwcHAAA9+/fx6FDh3D37l0AgIeHB+rUqQM7O7v35mHgzZs35fVStGhRuLq6as33uh+S5nd+ycnJePz4MQCgWLFieZ6PruP9Vc/Jqo4ePaqxnwUEBKBu3bqvVO+relvOdYZc168qLi4Op06dUjsmqlatKu9769atQ3BwMCRJQmZm5httK6B5TsuLvGz3wjje9V2nGBsb53l+b9P+otoeADAxMTHI9Wdu26gg2/1tcOrUKWzevBnh4eG4fPmyfM5WVaRIEZQtWxb16tVDhw4dUK1atTzVrS8w/rac6wrLkydPcOnSJQBAw4YN31g7DHFs5vfck9drmMKmuuw2NjYGP3++6ftbfeu5sM7J2q7rlMf533//jUuXLuHhw4dqZYyMjFC2bFnUrFkTXbp0QbNmzQzWnlfxtpyzjx8/jl9//RWSJGHJkiWvXN/rOv4yMzPla+j8XOMbwpu6ZnrT/iufm+/ycjx9+hRGRkawt7fPc5mkpCQcO3YM8fHxaNCgwWu/Tr5//z5SUlJgZmZWqJ9Vb8t1j5K+9rzuL26+K/v8m2gng2/vmBMnTmDdunU4ceKE2sVGgwYN0LdvX7i7u+sse+PGDRw+fBgJCQno1q2bxon00aNHiIyMBKB50CYlJeHkyZN48OABbG1tER0djYSEBERHR+PcuXN49OgRMjIy8Pz5cyQlJckX5w4ODihfvrx8Eert7a1W77p169C1a1coFApkZGRgw4YNOpdv4MCBsLCwQFpaGnx8fGBtbS3XI4RAZGQkzp8/j8OHD+P69eswNjZGyZIl0aZNGzRr1gybNm3C9u3b8eDBA1hbW6NixYoIDAzE48ePkZKSAj8/P5QtW1atffHx8ejRowfCw8MRFxeH9PR0SJIESZLk+Sr/GhkZISsrS04DtF+ECyGwZcsWrFmzBlFRUXj+/DmysrJgZGQEBwcHtXXWtm1b2NraAgBOnz6No0ePIi0tDb6+vvjggw9gbGwsz//y5ctYsWIF/vzzT1y7dk1e/rZt2+Lzzz/H6dOnsWHDBty+fRvW1tYoU6YMMjIy8Ndff+H8+fPIysoCAEiSBCMjIwghYGZmBgsLC3h7eyMwMBDdunWDt7c3srKysGnTJvzwww84e/Ys0tLS5GVWXT+SJMHLy0t+iNGhQwcoFNpHu9W3fI8ePcKqVauwZMkS3LhxA6mpqRBCQJIkmJqawtbWFra2trC0tISlpSXs7e2RlZWlsQ/WrVsXkiThyZMn+Pfff+V5lStXTq0tsbGx+Pbbb7Fp0yZ53xZCQAgBOzu7XPdrXVQv3kNDQ+Hr64v27dvDzMxM7ZgIDg6Wj4nMzEwcOHBAflikPPZUL7C9vLxgYWGBli1bolOnTjAyMlKrr1u3bhBCYNq0aejRowc8PDx0tvHRo0fYtm1brvNTPpxq164dXFxc9C736dOnsXv3bsTExKBYsWIYOXKkvG2TkpLw+PFjrFixAlu2bEFkZKTGvmtjY4M///xTLa1hw4a4evWqvD6XLVuGevXqoXfv3nBxccHJkyfVgg3Vq1dXuxFJTEzEyZMn1ZYxNjYWsbGxMDExQfny5dGkSRNUr14dVlZWALKPsyVLlsjHkZWVFWxtbeHi4gJXV1f4+voiODhY7eY2KSkJn332GdatW4eEhARIkgQbGxvY2dkhJiZG3q9yUj2PuLi4aDwMDAsLw88//4xTp05BCAFLS0s4OzvD2dkZlpaWGtsp5z76999/w8fHR+0zIysrC8ePH5fPERUrVpRvMqOiohAeHo4//vgD6enpMDc317lP5HY8xMTEYMqUKfj9998RHx+vtvwmJibycmRmZiIpKQkvXrzAixcvNOrJy0NSIQSWLl2KpUuX4urVq4iPj4epqan8uTJ48GBUrVoVc+bMwYIFCxAdHY20tDRkZGSoncv0ze/p06fYunUrBgwYAEmSkJGRoXf5gZefqfv378e3334LhUKB2NhYLFiwAOvWrcPZs2flc7K5uTnq1auHIUOGoHbt2jh06BAuXLgAAChbtiwaNWqEokWLaszj7Nmz6N+/P06fPi2vC9W/dnZ28PX1ha2tLaysrFC7dm1ERUWhWbNm6NChg3xe2rt3r9pnR8WKFREcHIyiRYsiNTVV58Ocq1evYtOmTWrl2rVrJ1975DzXAdnH5ePHj3Hs2DG1cg0aNIAkSXj8+DF+//13zJkzB5aWlqhQoQJq1qyJkiVLyvuhj4+P2jlQm4SEBHz99dfYvHkz7t69Kx+HpqamaNCgAYYOHYr27dtj/vz5WLp0qdbzkq4bytjYWGzbtk1+OHf79m35oaWtrS1sbGxQpkwZBAQEyJ8fERER+Oabb7B79255uyuZmZmhZs2aaN68OeLj4zF9+nQoFAo5+CaEkNt58eJFmJiYoHTp0mjbti2Cg4Nx7tw5tXNhw4YNte4vQPY5YMuWLTh69Chu376N1NRUuLi4wNzcHB4eHnBzc5OX7/Tp04iPj4ckSbCzs0Pr1q0xfvx47NmzR+8607bd//rrL/z66684f/487t27h+TkZI1rhfwGRbKysrB582Y52BIdHa12DOQ87+o73lu0aIFjx47pPTY7dOigsy3Pnz9HeHg47t27h9KlS6Np06ZymhACf//9N2bPno1///1XPteZmJjA1dUVxYoV07gmTEhIwMSJE7FmzRo8ePBAY1kUCoXG9WfTpk11XmPXr18fvr6+ePjwIdLS0pCQkIClS5fCyMhI3kb37t1D//79sX//fqSmpqrNy9bWFk5OTvm+Lsp5rNy5c0ftAb+trS2KFSuGihUrYsCAAahcuTKA3K9TrKysYGlpCXd3d/Tu3RulS5fGd999h0OHDqmtd1W6boclSZI/6/Vd++gLjGvb52NiYrBgwQKcPHkSjx49QlJSkry+9V3bafvSjr5jWqkwru30Ld+rfOkjNjYWf/75p9bPAGXagQMHULx4cVStWhXnzp3D+vXrC3RsFiQg6+rqipCQEKxfv14OfCspz73FihVDVlaWxv2MrmveEiVKoG7duujSpQtq1KghX3cqZWZmYt26ddi5cyeePn2qdh+3bds2XLlyRet5QNcyZGVl6X2esH//foSGhiIyMhL37t2T72+V50hd97eRkZFISUlBmTJlNJYB0H09YW1trbG/eHt7IzQ0VG09K5exVKlSKFu2LO7du4dz587le7vnhfL+SZIkREZGYuTIkQgLC1PLk9v1u6+vLxYsWIDKlStrPdclJSXB3Nwc5ubmKFKkCMqVK4dp06bJ58+c9x3KddajRw/Exsaq3Tdru/+YOHEiQkND5fVnZ2eHpk2bwt/fH1FRUVrPBRYWFvDw8EDlypXzfS7QRXn/u337dgwbNgySJMmfqcp9UPkcxNvbG/Xr14e5ubl8H1+uXDn5mismJkbv8TdkyBAMHToUpqamauti1qxZ2LBhA65fvy7vv126dMHgwYNhbm4OALh27RqWLVuGc+fO4dmzZ2rbSXlua9eundrnXEZGhs57zrS0NGzcuFHrubBDhw56v6Ccc52pnl/zct6yt7dHpUqV5GPe3d0da9aswd27d+Hs7CzfS6t+diiPz3379sHJyQkffPABevbsCR8fH53t1PY5/uLFC3l/cnBwQJUqVTSuDcLCwrReK/bu3Vu+Rp8/fz4AoGjRomrXBvl9DqN8xvT8+XN4e3sjMjISZcuWlZ/BqF5Hq7anTZs2aN26NczNzfWe05YtW4YnT57A1tZWPqcp15nyWackSUhOTlZ75pObb7/9VuuzIm1peQ0QN2nSBH5+fujVqxdq166tkX7v3j1MmDABGzduRFxcHADA1dUVnTt3xtixY+Hm5qa13oSEBCxYsABLlizBlStXAGSfCy0sLORzcvv27XH58mWsXbsWR44cwd27d+VzWvfu3SGEwMqVKxEeHo6nT5/Kz1eVx6eVlRV8fHzQv39/tGvXDkZGRrh48SLWrl2LFStWICYmRuMeSrkt69atix49emh8IULXZ5K+fR7Qft0DZJ9rdN0bJyUl4fjx4/jzzz/x4sULrffG2uTluk9Xe1TT8vLFzSdPnuDw4cM4duwYzMzMUKlSJY3naPqOXWNjY422nD59Wn6OZ2pqijJlysDLy0teBtXj79KlSzAxMUGpUqVyvd/Wdb7OTxxC1zrTRl+sJM8EvRUuXLggPv/8c/HFF1+I9PR0jfQXL16IDz/8UCgUCp0vc3NzMXPmTK313717VzRs2FAAEACEQqEQ7u7u4rPPPhP3798XQggRGhoqJEkSRkZGamWvX78uunXrplZWkiT5f10vSZLktinzu7m5iUuXLsl1r127Vs5vZ2cnJEnSeKmWV/5vZGQkevXqJR49eiQWLlwoXF1dNdqkLK+czks7W7RoIa+PsLAwYW5unuty6qs35zpwcnISpqameSonSZKwtLQUffv2Fc2aNdNYl5IkCTs7O+Hr6ytsbW311mdpaam2r+Rl++mqq1q1aqJkyZL5Kqecr6+vr/jrr7809u1WrVoJhUKhlt/Hx0eEhYWJvn37CmNj4wJvB9WXm5ubqFWrljA2NlZbHwEBASIqKko8ffpUDBs2TK0teXn5+vqK8+fP6z3GHz58KD777DONsubm5qJHjx7in3/+kY8J5fafPHmycHR01Hq8q+5bqu01MTERnTp1Ek+ePFGrT7lejY2NRfPmzcWqVatEUlKS2jlCua5zzkfbMal8GRsbi48//ljcvXtX63lLuW1V9zkfHx9x/PhxceTIEeHv7693n3FychKurq5a913VtqiWNTIy0mirtbW1GDFihNixY4do06aNsLCw0Lr+tG2ftm3biv3794sWLVrozK+cl7LtFy9eFGlpaaJ+/foFPt601S9JknB0dMzT8Zbz3FOqVClRqVIl0blzZyFJkjA2NhYtWrQQv//+uwgLCxMlSpTQKOfn5yd8fHy0Lre2/UH5GeLs7CzS0tI09ont27cLCwsLgxzPOdvSpEkTce7cOXleN27cEC4uLrmuT2tr61zr1nY+d3d3F7a2thr7pfIzRNXu3btFlSpVRL169UR6erq4cOGCxnFVqlQpncddbu1r3769uHXrljy/I0eOCBsbm3yfy1T3+379+onWrVtr3Z9U14eLi4sICQlRu3b57rvvtJ5LTExMhK2trbC1tRWlS5eW99d169aJihUrav0cVygUomTJkqJ58+Z6l0eZ19jYWFhZWYkSJUrI50FVR44cydM+6OTkpHN7GBsbC0tLS2FnZydq164tZs+eLe7evSuGDRsmzMzM8nRuUb4cHBzyvZ0kSRJ9+/YVffv2FSVKlNC7n2jbdkFBQWr7ixBC3LlzR1SqVKlA+4zqus85L+X/Hh4e4ty5c/JnkkKhELt37xZFihTJU/05zzU5j3dVf/75pyhTpoxGW/KzPDmvP5Xv68obEBAgZs+eLWbNmiV27twpMjMzhRBCjBs3Tpiammpc4zg4OIjSpUsLOzu7PK1bhUIhrKysRMuWLfN8DlVdfnNz81zPL9quuZ8+fSqGDh2a7/XXokULcfXqVSGEEBEREWLDhg1i8+bNIjIyUr7WUr0ezst2cnJyEk2bNtV7nZLbNlWeh5THqYmJibCwsMjTeVbftY/qfp2TatqVK1dEuXLl8rTdVPcZY2NjUbRoUY12KF8NGjQQffv2FW3atBFBQUFi7Nix4vTp07le2+laVm3Xdrdv3xa3b98WGRkZQgghrl69KkqWLClcXFzkdp05c0brPYJyfvb29sLR0VHY2tqKmjVrijVr1gghhDh37pz48MMPhZWVldY2lS5dWowdO1bn55q+a1Vtx+bZs2dF48aNtV5b6jv3KNdNXj6nlWV8fHzE33//Lb777jvh5eWV6/FjbGwsWrduLSpXrixfM3Ts2FHjWMnv+SxnGW37UV7rzFnezc1N/ixTHh+DBg0S8fHx8v7Tr18/nes75zrOS/t1bXtlPW3bthUJCQlaPyN0uX//vmjZsqXGPJT3YvraYWNjI0qVKiXMzc018mk7/nI7f65cuVIUK1ZM67JqOyc5ODiIHTt2CCGEuHjxojAzM9Moo2376dqPVbdlr169xKhRo0TlypWFtbW12rVPcnKyCA0NFQpF9vMZbc+0VK95c+7H+taF6vJ5e3uL7t2767xmV10f1atXFw8fPpSvbXx9fTX2ceX/xYsXF9u2bRPDhg1Te96g67NFdfsVK1ZMWFlZaRxH1tbWok2bNsLGxkbn/urh4SHCwsJ07os57xMUCoXGeUvb9tPVVl3rrHnz5mL8+PHy55K288SAAQM02vf06VPRu3dveZ3mZV4KRfaztj59+mg9/0hS9j2umZlZrtdeOetUXm+oevHihahWrZrONllZWYmBAweKRo0a6b2eyO2clrNNpqamYsqUKUII9Wed9vb2YuDAgfIzH1WJiYniu+++UzvGlPVpK6dcdmXaihUrhCRpPsfNSfU4KVOmjPj+++/F7du3hRDZn8PKZy7a1rmRkZFYtmyZRp3nzp0TpUqVyvUzSHlO0pae188d5cvS0lI+rvNTztfXV3z11VciKChIODg4aD1/qG6/vByXQgixa9cuUa1aNbkObffGOT/HVc/dK1eu1JhXdHS0+PDDD7WelxUKhdq9v+p1Zs59ydLSUl7G5ORkrcuVlpamdX9WllM+R/vkk0+0HrsKhUJUqFBB3LlzR+1ZZuPGjfUeu+3btxeBgYFar0GU58mc93rKewh952tt18o5zxP6rtu10RUryQ8G394SEyZMEJIkifr162ukpaaminr16qntVCYmJsLd3V24uLiova9QKMTEiRPVyitPpDk/xJT5bW1txYoVK+QdSnUH/PrrrzUeGuTnpa2csbGx+PDDD8XkyZPFRx99pLWciYmJKFKkiKhZs6bWAJiynZ6engW6YdD1oaBQKESNGjXEwoULNepUXjjnp153d/dXCuC96geTthsYQ7Ql5za2t7fX+YGac/7GxsZi6dKlQgghsrKyRMOGDTU+5JXTum5SzM3NhZGRkUG2uXK7Fy9eXBQvXvyV1tHXX3+tduxNnjxZTJ48WbRr106YmprmWrejo6MoX7681jYqy1paWgpnZ2fh4eEhnJ2d5Q9UbduldevWYvjw4WrrU/XDyMbGRvTt21csWLBAuLm5aXxw2dnZiQoVKohatWqJhg0bilq1aokKFSpoBMqVN94nTpyQlz3nts25fW1tbYWxsbHWdZLX/amg+7Wui+u81C9JkrCwsMg1IBwWFibGjh2rMQ9tgXJJyn4wqu9cYYjjX3X5lem6LnZ0HS/5mX9AQIB4+vSpvE/s379f5znUxMTklYPsCkX2w+mdO3eKuLg4rTe9OdeBtuXMmWZtbS1q164tSpcurfWhd871YmJiIj8IVb5q1aolJEkSZcuWFZMnTxZDhw7VOX9jY2NRrFgxUbFiRdG5c2dRpkyZPG1rd3d3ERkZKZKSkoSnp6feffhV1rO2/UKSJFGlShUxefJkERQUVOB6DNUu5cvR0VEsXrxY3gcfPHiQ6xdgcr5MTU2Fv7+/3uCgcrvl9iArt8B5frdPQfOq7i9CZN90VahQoUDbwMjISOOBec6giur26NixY77n4+bmJkqXLi2sra3VPneUx7uqxYsXa2wLX19fUatWLWFiYqIxb237gyRJwsvLS+fnq76X8pxavnx50bNnz1wfSBXGtta27lVftra2wtPTU3h4eORar74vd+X2MjExka8tVK/nlPcV2u5L8rqcOa9TatSokWsg18LCQnh5eemdj/Iey8bGRuMz2cTERCO/sbGx8PX1FSVKlJDf8/PzE1WqVJFfygC5vm2Sc7tZWlrmui60pedc18qga85ru/LlywsnJ6dc22JnZycmTZokli9fLh93U6dOFcuXLxdTpkzRaEt+tp+yncHBwXI787rdtaU5ODiIpk2bis6dO8sPwbStm2LFislfSsytvUWKFBGVKlXS+sDf0dFR9OjRQ4wbN06MGjVKODs769xGRkZGBToXdOjQQYSEhOSpnLGxsdZrZ1dXV1G5cmWdZZTPE/LTLl37cs7jWJIkceHCBXkZcjv2dO3T7dq1E+PGjdNazsjISNSuXVt07NhRVKtWTf4CnkKRHYATQohDhw7l+vrtt9/k+xtt87GwsBCBgYF6ryHs7e3F6dOnxdmzZ8WgQYPU1lF+z3F5fanWa21tLa5cuSL69esnp5cpU0Y0b95c6z2Gra2t2n2e6gN0bW3Vdk9nZmamFiRUngvt7OxE48aNRePGjUXNmjVfeflyW/aiRYsKGxsbedvXrl1bpKeni5o1a8p5VLddzusBffufajl990j53b4KhUJ069ZNfl6g+lK9T1Btv7b5BAQEiC5duohevXqpreuCXMPoejk7O4vJkyeLGzduiMuXL4uSJUtq7N8Fqdfa2vqVv5SpvDdftWqVfC2YkpKS7/Oa8nq/aNGiOrexj4+PmDRpkmjWrFmu9TVs2FDtWafqtnN0dBSNGzcWN27c0AgQ6zr2FAqFKF26tJg8ebLa/YfqvbwkSeLGjRs6nzlrK2dkZCQaNGgg3Nzc5Hnb29vL81bdPgqFQnz66adyfQ8fPhTu7u4G28/elpckSaJbt25i+fLlaq+c1z0KRXZwWlfwp2/fvkIIIZYsWaL3PGZkZCTWrl0rhBBi+fLlIiQkRO/nkfJlYmIiqlWrJvz8/LRur5wvS0tL0apVK/n5xMcffyyEEKJ169YGWW/u7u6iU6dO+VrPyr+6nts4OjqKy5cvCyGE2rlH17lYeV+a87mS8l5dNQ6h3E7azsE5aYuV5BeDb2+JBg0aCIVCIX788UeNtJkzZ8ob2t/fX4SFhYmUlBQ5PS4uToSGhso3lEZGRuLMmTNCCCGSk5NFmTJlNE7i7dq1E0WKFFE7OTRt2lRthxo4cKDWDwBlOSMjI7VAQW4Hlbb3c7vRUp7Mc7ahYcOG8k2wsi1A9jeQ9H2rsEqVKmofJsqXqampqFOnjjwf1bqVeYTI/kacanmFQiHWr18vrl27JpdVfQhlYWEhzp49q/Vbrv7+/qJfv36idu3a+Vpvym2g7eLfxcVF+Pj4yP/37dtXzidJkvjhhx/EgAEDNMqZmZmJ9evXi5EjR2ptR7du3bTeVLZt21akpKSI3r17a71QUN2/bGxshJWVlXyBdOnSJbFu3To5XfmwWJIkERISohZcU71hkCRJ7oGUc33XrVtX67dmta1LU1NT8fXXX4uRI0fK33zRlm/Dhg3i008/1dhXixcvrvawRfnq1auXfGwa6iZLtR5d3zZ5lbpV/2/QoIFYu3at1t47qu7fvy/Wrl0rWrZsqXbj8+zZMyGEUNu2JUuWFN27d5fnpzxG9Z0bVqxYofXiV1teXTfkum6EJEkSLVu2FCEhIWLmzJnyOixevLgYOXKkGDNmjChfvrzaOleWrVWrlli4cKG8bD4+PvL+o7rPfvTRR8LFxUWYmJiIfv366d0Xhg0bJnbs2CHXqXrMtm3bVjg6OgpJkjRuIpQ3jtouQCQp++F6bjde2so5OTlpXAA5OjqK1atXy/PT1rvQ3Nxc46F7nTp1RGZmpkhKSlJ7iOvi4iKmTp0qjh49Kp4/fy5u374tX2Sqru+vv/5aVK1aVW+7JSm7d6+yTfb29qJdu3Zq60k1IKRcnznPoVOnThUZGRlaP5t0reeCHG+5HbOqeVJSUvJ1U+rv7y9++eUXtYtZScr+5pgyT/fu3TWWo06dOsLNzU1ve7StFyMjI7VvDua8EX+VdaarXIUKFeR0fQFW1TZ9++23QgghWrRoIb9vY2MjQkJCRFhYmPj555/l93PuF8plUD7UUU3z9PTUuLivWrWqWLVqldizZ4/YsGGDvH4UCoUYOXKk3gc0r/KZoa+sJEka21eSJOHv7y+ysrLEb7/9plG2du3aYsyYMaJ48eJa61QNJGo7j+hqV27Lp2s5lJ8l/fr1E127dpXPf/b29uLOnTtCiOxv+yt7HdjZ2Ylp06aJBw8eqJ1fTExM5G9iSpIkqlatKhQKhZg1a5baza6FhYUIDg7W2sbKlSuLSpUqaQ3G5Gy/8vont+1Xvnx5MWPGDPnbzkZGRmrTX375pcbxNW3aNHHt2jU5cGpnZycHF3LbH/Qdm/q2kSRJYsOGDVr3i5zfVs9tm6peC2zYsEEMGjRI7dylPNa1talUqVJqvTc7d+6s86GVtvaULl1a41iXJEkEBwernYMN8XAxr8du6dKl5Wl/f3/x7NkznfNUfulJ20MvbWVq1aqldm2net2el+XSdm5/lWtPU1NTteNHXxu8vb3laUtLSxEaGqq3nSVLlhQTJkwQjRo10rteTExM1L7cUqZMGbVrJuVy9uvXT2RkZIjAwEC18nZ2dvK5R/WaV3m8K++fdH2ZLOdnh7b7rCFDhghHR0f53lOSJNGzZ0+NfObm5uLu3bvy84SvvvpKvj5XKBRi69atWtexchmio6O1XvfMnTtX536i6xzSrFkztfU6a9Ys4ejoKJycnMSCBQvEvXv31I5t1X1i1apVWs+Xys8qfceQtbW16NWrl1i5cqXWZdd1/1TQ65SpU6eK3377TYwaNUrtXFKhQgURFxcnfHx81NabmZmZWL16tfjhhx/U5teqVSut2zTn8mlro5GRkfjrr7/Etm3bRO3atYVCoRADBw6UP58lKbv3svKLFpIkyQE2SZI0RkxSfbh4//598fvvv2v0zq5ataqoVKlSrutH13lU30vbPvjRRx/JIwDpWi/Kebm5uYk2bdrI/3/yySfydJcuXUR4eLj8/6NHjzRGpFG9VlYoFKJ79+7CxsZGvr9TKBQiJiZGazv69+8v5s+frxb4VL7KlSundZ9XnVd+7+1V71EvXryosQ0lSVI7dypfyi/F5GU/03XMK3uLK9eLJEli6tSpauUlKXtUDm3PlnLuI4b6bDU2NhZ//PGHEELoDI5VqVIl17YoX6rHtaurq9b1Yoj7QuX9hSRl9w6rUaOG1mNM22evclvkvO5s0KCBWLJkiYiLi1M7xpV1NGrUSK2Xrmr5cuXKic8//1yuq0qVKmrbXaHI/tJMRkaG+PTTT9Xap3x2I0nZI0JoW+a6detq3TeV5fbs2SN+/fVXtWMbyP6cy3ktrHx9//334uTJk2Ls2LHye6rPZVTvVVVfud1j6zsu9V1DK581KBQKsXfvXuHh4aH2fFmZ1rVrV/nL6E5OTiI2NrbQrjV17Uu7du1SS8vLF+D1rYfc2q8M8qp+AczV1VUkJyfrPCcoFApRv359uUORsbGxqFOnjtr6zHlc6vuMz3m8a9vOOTH49h/i5eUlFAqF2L9/v0aa8psQdevW1dlVVIjsh+HKevr37y+EEGLRokXyTqIcRkG5w6Snp4u5c+cKe3t7jQ/LXbt2yeVq1Kghfv/9d7Ud28LCQuzdu1dtJyzIB2h+8lasWFG4u7vLN8fKC1Vlmxo2bCg/5FUNwBkbG8vfvO3atat8c59z3iEhIWofEpIkiZUrV6odZJMmTRKSlN1rRzlkzaRJk4QQLz/MDh8+LA+5omyX6rpVfjPX2tpa3LlzRxw4cEDrCUF1mFBluqmpqYiIiBBCCDFs2DCtJ8ITJ07IN3QODg7CyspKVK5cWd4HlN/eNDIyErNmzZK/jfnDDz+IoKAgIUmS8PT0lC8ylBdPH3/8scb8nJ2dxc6dO+Vl9/X1Fdu3b5e/ZValShW5x4dyv7G3txcKhUIMHjxYnl/JkiXFv//+K9ej7Amq3H4KhUJej6onyUmTJomMjAxRp04doVAo1Oal2s7Ro0eLevXqabS/RIkSIiMjQ3zzzTdq67pJkyZq21116Eblq3379vJ2z5k2fPhwOa0wPiy1vQxVtzJg8uuvv4rnz5/n6fy1fv16YWJiIhSKlz1vVbet8gsCyvpHjBihdd9V3bbVqlWTv8Wb88O0ZcuW4tChQ/JQH61atZLTVW8SlUMhaVvW4cOHi+fPn4shQ4YIScoOVCkDh0Jk99wLCAgQkpQd4FVeMCkUClG5cmUhSZKoVKmSyMzMFBcvXpSHsVRdHoVCIUaPHi0vu+r8bW1t5f21SZMmokuXLkKSsgNsN27cEOXLl5cvbqOjo9UeRnh4eAhra2t5fgpF9pcAvvjiC7WHOarzLF68uHx+U31feXGfszejMl11qIKNGzfK2+eff/4R8+fPV/vSgmo55cWsJEli0aJF4ocffpDTGjduLLKystT2oS+++EI+tx45ckQEBgYKSZLkc4lCoZDPnZIkiW+//Vbj4b+5ubm8HZTrwdTUVNy+fVseJkKhUIjo6Gi1B2zKb8AHBwerncvzezGZ32M4t+Nb+eBA9XxVvXp1te3ep08fjQCA6lCuyvPkuXPn5DqV61r1iwrNmzcXSUlJGttdV3tVv307fvx4vctR0PPQzJkztaar9sC4ePGi2Lt3b55unvbs2SPfSFtbW2u9IQVeBrV13XwoA46SlP1FkEmTJmnMa8yYMXK9yuGqlMNYKvOoHqvKz2UHBwdx5swZ0bx5cyFJ2V9UuHDhgrh165Zo37692nyU59wuXbqIW7duiVu3bqlda1SoUEFnDzTVL5xs3LhRtGjRQq3sN998I7dfNZAtSZJ8zOfnei8v+XJeS65du1Yj+KJ6U1SxYkX58+HLL78UQgj5+s7NzU1ERUXpPL+ofh4ph9g5cuSIEEKIqKgo+T3V4QiV20x1fps3b9ZYDg8PD9GjRw/5fxsbG2FqaioqVqyokdfU1FTtod758+fleVtaWqoNj6gaSFAefyVKlBBJSUnCw8NDKBQKsW3bNnm4TW9vb70P717l2FS97lPdx5o0aSIiIiJEmzZtNI6ZH374QW1fUt0nJUkSQgjx448/qu2Hyocvyv9zjkBRv359ER8fL19HK69fZsyYodFu1UDq0KFD5etMU1NTMXXqVFGuXDmNfdDQ11i57fPnzp2Tg2IKhUL+zFXmVX1gmrNdqg+Zli9fLjp37qw2L0tLS7F9+3YhhBCHDx9Wu26fN2+e1jpza3tuefVd80lS9sNa1UCVJGUHlFU/AwHIXyJV7jPLly+X61H2rNG2TpXvKc/ltWvXFmXLlpXfHzx4sDAzMxM1a9aUv1Xt7OwsFAqFWLZsmdo+cfbsWfn+pUePHvLwl8pzQc5rXuU1TKVKldTarrrvDxkyRON4UA2SAC/v4zZv3iy3Rfl5Z2JiIubNm6d2H6f6PEH1/kj188Pf318cOHBAbRlUA4s9e/ZUe56gnO/hw4fVvoCqa/ubmJiIsLAw+X9HR0dhZWWlNjy/6nZXDk+vUCjk7Q9AdOvWTeN4UNYZGBgo5s+fr9EeZT1eXl7yMK3t27fXeky/yvGd8+Gb6meBQqGQ221hYSE2b94s3N3d5Xsk5X2Hi4uLfL7XF6BwcnIS169fF5s2bdIIhEmSJD+HyMjIEP7+/vL+oGyL6rlu/fr1QgghByebNGmithw5Hy6qDh+mfCnv8/J6DsjrOlYoFPLxpzpUYN++fcXff/8t/6+8dpIkSfTq1Uts2rRJ7f7f0tJSVKtWTV7HkiSJWrVqiaysLLV7ASFefsFd9Tyr2p6LFy+Kq1evqt1/bNmyRc5TtWpVUapUKSFJknB2dhZPnz7V+NKOJEli9+7detdZfvdL1XRlO3NuQ9Xyym2vPOaVX4jN+ZoxY4aYNWuW2ns57wdU01S/LKp6H3vo0CF5n1c+U9S3LPk9/nKWU11eZ2dnceDAAbX9RfVLs6rX+6ovbcNPm5ubCycnJyFJktZgnqGvD4yMjETHjh3Frl27RFZWlly/6v2Cth6bZmZmal/EUT0XWlpaiuDgYLU6lftMbGys+PXXXzWekeX8DFXev/zxxx/yMxCFQiHatGmj9sVgd3d3tQB3VlaW2kgsyi/Jtm/fXkydOlV+39raWpibm6s9W1Xel1SqVEkcOHBAPq8p74+UdSnrUH6RQHm9r1wG5Wewti9zWVhYiDt37uTpWNN2XOo6lhWK7OC+8jq8VKlSwsTERIwZM0bjuu/ixYtqX96ZP3++Rr2FdQ2q3Nb169eX/w8ICBDHjh3Tei2lr015aaOyzri4OFGqVCm1Z3yq17wKhUL89NNPokuXLmrXTarzaNq0qfjyyy/V9tec7c3tnKrrvKt6rtD3+VgQDL69JZTDbZw9e1bt/WfPnqkdHNpeBTnQjI2N5Ze24fuUH7SmpqaiePHiGkNY2dnZiZIlS8q94FR3ejc3N/lbP6rfqFAoFGLJkiVi/fr1cg8o1QDT5MmTxcGDB8WBAwfEzJkzxQcffKBxEaT839LSUnh7e6sdsHv37pXXm+rwGsOHDxezZs0SkpT9rdKKFSsKhUIhPvvsM7V8RYoUUbt4/uCDDzQu0qpUqSIUCoX4/vvvxYABA9QuXFVPolu2bFE7sSvrLF++vHj8+LHaRbjyAl35jUdlQCE4OFguq/qAYt68eUIIIV9YKtOVF1SjR49W+2bF8OHD5SCi6rd2+vXrJ4QQomvXrkKSsgMayputX3/9VR47Gsh+mKT8Rq5yfsqH/9WrVxeSlP2QUDnEnHJ+Hh4eomTJklqH2lLd95ycnNT2MeXFnGqPN+XDTuVydenSRd7eyvkpL5y8vLzUekNVrlxZ7qLctm1btYuYQYMGidq1a6st26pVq9S2u/LGTfkbRUB2MEN1u+fsbTJo0CC15f3ss8/EsmXL1E72CoVChIaGyt8Wy7nMqvlU9wFnZ2fRp08ftZcyrX379qJPnz5yD0htQ/kpL1qKFy8uf/vkf+y9dXhVR7c4vGafuBvxECFCkBACQYMEC8UlLbS0DQ6hWHEr7i7F3Z0WLwUKtDiUAg1SpDgU1wCxs74/zm9NZvbe5yTQvvftvR/refaTnC0ja5bPzBoxmE712dnZoaOjI/r4+GBoaKh0UZpEusTVb2IfrKys0M7OTpog0FvVKsqQxo0b8/87duwoyQE/Pz9+hgMZMaIz+vPPP2NwcDAqimliYu/evdJKRhEHdnZ2fKfA4MGDNXLZnMJXGxF5GSd6xsCYMWNw/fr1nC+JPmn387Rp05AxU3oLRJRSwRw+fFiiGdHxmjNnDpcjomyndqrlPWMMq1evjkajEXfv3s3TpInf0f+0m0Lt6F2+fBkHDRoknfMi9tfZ2VnaxXjt2jUNrgsXLoyKouCYMWMQEXHLli18jET50r59e2TMNClHRnqPHj24M6LuX48ePRARNbKcnAXGTJNHjDEsWLCgxNO+vr58YpdSb9FKvfDwcBw6dChWr15dQ9MODg7SLr/AwEA+UbJgwQLcv38/lwXiNXr0aNy/f790UbDV3t4es7KyOM2I43706FFpwYnIiyQnxf5TOq6ePXvyHYEODg687/Rt48aNuXPTunVrqU+iEauepEpISJD6EBsbi4qiYGJiIu7fvx/Lli2LAKBJp1m5cmXpO3ECgzFTyk5FUaQ0EUSHI0aM4DRqjm/F+3r8LvZJURQewHd1ddWkZxPfI33++eef8wwEiqJghw4dEBH5hPDw4cOl73788UdpzIiGxQDqnDlzePt27dqlKzcp0wEi8vuFChXC7OxsXLZsGacfMW2aiJegoCBJR5crV07CC/WJ6rt48SJfYS+WxRjDjh07IkCuviYdt3//fhwwYAACgNQONzc33Lt3rzTu4thmZWXxALiYuk9Pzvr4+HB95+npKekr0j/u7u4a25XkiIjHGTNmaMovW7Ysjho1itMiYu7uocDAQAmHHTp04PxiMBgwMDAQjxw5IuGR/nbp0oUH9fv06cPbQ2mLKYBMk+WMMdy8eTP3G2bOnMm/OXPmDLc/1WcFeXl54fTp0/lq3JSUFB5Q6d+/P+7fv5/bTepUk4qi4LJly/gYkd0YHR2NCxYs4P3y8fFBxNyFamIZtWvX5nTTsmVLbjvS87t37/KzbMgmoyAPtaFfv3549uxZKfgaHx/P21OoUCF8+/Ytl3di/TY2NlxO085FKhMR8fbt23wc6PL19cXly5dz34BSQSqKaTGIOKEo7lqaOHEirlmzhl9du3blz5ycnPKkeXHxjZqnKXuFp6en2cU2QUFB/AzXatWqcdlibW2NK1aswFatWkl6VdQP9Fd9thrRDWMMR40ahUuWLJHSL9FlbW2N8+bNwyVLlvCLztOpUKGCRFtFixaV7OqmTZtKbRHHuWnTppiQkMDtePqGJhQrVqyIjDEMDg42eyaUnZ2dxtcIDAzEZ8+ecd4nXvrtt98kmhDtyPLly3ObzMHBARMTE7kejIyMxMTERD7ZrmfbEz2TLv7ss8/MpkMlP07vzCyyb0Q/Th1PIJtBnLhdtmwZIiK3I6OjozlvBAUFISLizJkzOU+p/VsqJykpSRPwNfc/LUokINoKCwvD7Oxs3gdxkcH27dslfhAnEGnct2zZggAmm9fSWWD0197eHmNiYjA2Nla6qP/Ozs4a2SmWExoaikOHDuUTXvmJx5jzEdTjqX6nbdu23EcZP348IiJfhCzaukWKFOGT0OLkMl2kOwoWLMjfo8xFRBt0if5Uq1atOC4KFCjAg9fu7u5SZh1bW1tJFlGQXIxppaWladoVGRkpnZ8l8t+pU6d4X9zd3bFZs2b8u99++43vaDMYDHj8+HGzsQa6ChQogKGhoRL/h4aGcr6lRTLqcSMbU5woENty4sQJvHnzpjROpAtcXV25TUqp54iXPD09pZ043t7e6OfnJ116aZSdnZ2ld+h+QEAAt3fIvhG/+/rrryWeV8dUaJGYuAhRfE42VLFixSR/R6yH/AVakEBxJNLnYnkjRozgscAOHTpgkSJFeP09e/bk+BPxs2XLFhwwYAAyxqSFEb169ZImpKhO+nbEiBEaH1CP97p06YILFizQ2Mpqu42u6tWra3Q42bvbt2/HqKgoXZ5XFNMkc3JyMrZq1UrjX5A8ofFVFAVXr14t6Qj1jjIqVxwLPT4Q74s0I6YQtSTL+vbti7du3eKLS9Q+1tChQzW4Fne4UXzH398f4+PjJdx27dpViq2Sb7V06VJJz9FZ63QWGpVRpkwZRESuV2lBDMUuxIkexhhfeDtkyBDeXr1jT4oXL47R0dH8Mne2nZubm/ReYmKiZO/Y29vrLp5o3LgxtmrVissm0Z8nG5f8ZZJ5jJliX23btuWxXTEbRcGCBXHChAk4ceJEnDhxouRPT58+nS/yIfuDdIS7uzu+evVKYxOqZQVjpkUzarrX411PT0+NzRsaGirZzKQ/xEXBRJ/5SY8u8oA4D1GkSBHcv3+/JDPFhflijF4tf/38/DSxT9F3fF/4MPn2L4ECBQqgoij4888/S/cvX76sUQDqKz8E+Xcuc/WoHbUdO3bw4Ih6FTopEtrZ16dPHx5MoXfEwAcBne9RsmRJzQoHdfmiAScGVnfs2MGDHo6OjtxA2rFjB75+/VraJVChQgX+/7fffqtRIOSEHT58GDds2MAVCKI8+fbixQtdB2r69OmIiDxXc6lSpfjORlqZRkovIiJCWlEk7pJBRH6wNfW/S5cuyBjDihUrcieL+v/rr78iY/L2eRoL2tUYFBTEnbZDhw7h48ePeR9sbGyknT0UuGUs9zyJqVOncvxTfRQ4tzQ5IeInv3RI46euj+qaOnWqtJLF2dmZK1kS/CJu1buITp48KY07nS1IK6cATI60etzF/OwUvBCVLu1WFPmGDHsKKIuG0fDhwzEqKkqXB52dnXHgwIH44MEDTTsQka8EV6fhFBWpaGA1bdoUR44cibVq1ZJSCuZXHvzdS8QJOf2KYppUF+sjBwLRdIaY2ig4d+4cP1uiSJEiiIh85+vXX38tpWUVy3V1dcXu3bvjqVOnePlqw9wSPizhRM9APXr0KP7222/cIBTpEzF3ZbqLiwuXPVTW69ev+aokKo/G/fjx41wG08Rq0aJFLY4ppeQj8PLyQsYYX0Gr901KSgrfhSsCpQOwdF7Nhg0bpAOIEZEvOjh69CgiIseNyNMiXXt4ePBx/emnn3D//v3cIBTrokUZallOgTDGGF+lRzxNCxgUReETivb29oiI3BnYuHEjIiIfB9FYJ/yTsSwG+Gn1q15wWL34BhG5PKB0tqdOndId97/++kuTclmUk2L/SXds3boVN2/ezN/PyMiQeGnHjh34xRdfIGMM69atywP1n376KZdLeuNrMBiwf//+mJmZiYjIz6alVDCLFy9GxpgUWGCM4dq1a6W+0yQ69YV2xVAqJ7H/v/zyCzLG+EQvTWTUqlVL15F0c3PT5XexPtqNbWtry3VAcnKy2b5HRETwA65Fp4acWtqxR/29fv06/9/FxYU/Cw8Pl3QxwalTp3TlZkZGBn+H7vfv3x8RZV386tUriXbMjd8333wjjYN4FpOiKJiVlcWzKlDAiL6lyTdycsUxoraICzEoeCyC+jtxIcK76B9zslltuxK9EG8imuSoGs8rV67k/O7k5ISIuZkpJkyYgLt375beFycORo0aJfG8iM/du3dj9+7duf1G7RFXwor6ijGGWVlZPPhYrVo1SRaS/amme5JL1apVQwDAUqVKcblEcldvjMRxJyCanj9/vsZWRES+26Z58+aSE0525IEDBzAjIwObNm3Kn4WFhXEZs3r1ak0fFMW08wYRsXfv3tJ9OkNuwoQJkrxT45oWb7i4uPAADpWJiHwcYmNj+a5rg8HAz8Lw8PDQ+Do0GSb6PeLiFETT7hF61qVLl3eieTVPizqQeCIiIsKsrm7WrBnOnz+f60eDwcBlMulVUT/MmjWL74R3d3eX+qVup96EkJh6nUDUAefPn5eCLuIu+bVr10ptETNwrF27FmfPno2MMSnIRHqTnkVEROCrV6/MHlugputRo0ZJbaVg+bZt2ySaUH+npk01jiwFMOkbURf/9ddf0qJKsbwdO3bo4ppoV/Tj1PEEshlE/49sN1GeUbk0GU1+kDjhrPZvg4KCeB/Gjh2L7du3N7sLvVy5crhp0yYuR8i2oUUq1AdRn+zcuVPiB71xF+1oUbbnRz/QOOmNnx7dkB5X8+67+gD51V+7d+/m8ZIKFSogYu7k1KxZszSTBHnRnbk+W6JR9bfis7z6TP4MyQr1O+J5vHr8Jy5SEzMKZWVlYU5ODl8QTTsJ89Mm8Xl+xoRiU4cOHeL3KHbFGOOyUBwnwlNqaqrGH1XrD/VRKCLtmWufJdtGDw+MMT5ZLe5oEXUjZQ2gBZ/0zrhx43gsB8C0yIR2J7Zu3VpadCnWV7JkSb5YSVEU3LBhg7QAYMiQITyOVKFCBV7mgQMHuB8r2iGkq8jeUMdRyAek98VF6IcOHdKdfFPbVjR5QzKtRYsW+fZ11LpRlGnqMRbHSVEUPnFYv359KeYj1lu4cGGNf0Fj17lzZ4vtzEvOvKtdbWtriw0aNJA2UFBbRF1FuBbjF6SnbW1t+YSv+K0YW1Xbp2IWNrI/RVx4eHhI46e2oUWZpiimBZOMmWKxot3j6ekpTWCL50hakp3qd/IjY8Xv8iO7FMW0qEG0Tyn1oshfK1eulOSqaIOeO3dOsjfEdrZv357La3q2Y8cOqWxadCxOgKtjUCLvdu/eXSPn1fXmJevyi0faSCDGeESfKL9y1dwlyp33BQU+wL8CgoODAQDgxIkT0n1PT0/+v6+vL1SpUkVzERgMBrCxseG/7ezs+P82NjZgbW2tqZcxBlZWVmBlZQWKoiUHX19fCA4OBn9/f+m+n58fBAcHg4eHB7/30Ucfwblz56B79+6QlZUlvY+IAABw5MgRAACoUaMGAAA8efKEv/Pq1StN/VlZWcAYgzlz5sCNGzegTZs2mncAAIxGI1SoUAEmT54Mf/31Fzx79ow/CwgI4LjIzs6GN2/e8L7Z29vDrFmz+LvUPgAAKysrTT0ZGRkAAGBra8vH7OnTp5r3xHEQIS4uDgAAKleuDAAAN27cgLt37wIAQPny5aV379+/DwaDgf9u3LgxAABcvHgRALT4ojKvXLkCRYoUAQAT3gMCAiAnJwcAQBrjzMxMAAAoVKgQAJjGgp5nZWWBs7Mzf9fZ2RmcnJyk+qpWrQoAuTgR20/1Mcb4PScnJwgICDBLZyKN0XcifYn/A4D0PtVHdFa+fHnw8/PjzxERjEYjAABYW1tDlSpVYMKECfzZq1ev+LcAIOEdAODRo0cAABAbG8vvUXkiNGzYkNeBiPDy5UsAMPFGVlYWzJw5U2qnCB9//LHUF8YYfPPNN3Dx4kU4cuQIpKamSvLg5cuXMGrUKPDz84O6detqyrO3twcAE80TPhVFgT179sDo0aOhSJEiUjs2bdoEgwcPhr/++gsGDRoEAwcOhGLFimnKRURd3vgnoUCBAvx/R0dH6Zk4BkQT1A/qZ4UKFQAAOG+RPGjRogUsWLAAAEzyUezf8+fPYfr06VC6dGmIiYmBiRMnSvRbunRpzr8EJUqU4HJY/YwgISEBoqKiNPffvHkDb9++BQCTrBHpEwA4vxF/kdwCMNGn+FsEol1EhKdPnwJjDObOnQs3btyA0aNHQ2hoqOabIUOGQIkSJbj8TE9PB8YYzJ8/H27cuAFt27bVfLNs2TIoVaqU9B3hiTEGrVu3hpEjR+q28eOPPwY/Pz/o2rUr13m2trZSP2/evCl9Q/KF6DozM5PLriNHjkCVKlXg559/lnhExJ8aRBlNspRwPm7cODh+/DiUKlUKrl+/zut78uQJPH78GABy5Sa118rKChhjwBjjOvDKlSvg4uICRqMRMjIywGg0wq5du3TbAwC8bBFIHpC8ffDgge63Pj4+sHDhQume0WiUeEndX39/f0mO3rlzR3ovICAAmjRpAgAAZ8+ehRcvXgAAQI8ePeDixYucl/TaPGbMGPDw8IBhw4Zx/gsMDAQA4PxA8pGAcEpw//596Xfp0qUBQB8HpK8yMjKgcOHCAGDipV27dsGOHTs07z979kzD72ogmnZzc+M6YMCAAXDx4kUNrgFMunfIkCFw/vx58PDwAESENWvWcBrct2+f9H5AQAA4OjoCYwy+//57KFeuHC+H9LNoS126dElTJ0AujYhANoCoT0iWkh5o3769bnkjR46U+Fqts9++fQsxMTEAAPD69WtYvHgxfzZv3jxeX37aGRISotsGEUqVKgUAufTAGANnZ2cICgrS2CUiWFlZgbu7OwQGBvI++Pj4aGxXopcDBw7we3p2XXR0NOd3kh+kY0qUKMH7R/o/Ozubf1uxYkWz7fTx8YFq1aoBgGnsqT2nT5+W3hP19f3797nev3jxoiQL9exPxhh06NABsrKyYP/+/bwu0jckE54/fw4Asv9AQPqK6gcAKFmypGQrkv4hWm3Tpg3MmTOHP3/z5g0gIjg7O4ONjQ0MGzaMP7t+/TqXgeHh4Zr6RahZsyYwxsDBwQEQETIzM8FoNEp2nx4UL17c4nMah3v37sHRo0dh6tSp4OjoyGWIq6urxtchWaBH33qgZ1epQU3zALk8LerAe/fuAWMMVqxYATdu3NDl6XXr1kGHDh3Ay8uL66MHDx4AImr8DgCA1NRUuHDhAjRq1AiePXsmjTuaFu1abDvhUARRB0RHR0syQ7Rl1DpA9GUKFSrEx0/kT6IZevbgwQNwdHSEV69egaIosHLlSmjZsqVuW41GIyxcuFCyYaic3bt3m+2PGg/q//ODJwBZF/v4+MD+/fs1tiT5cXpA8kf049TxBNKXrq6u/Du1XWljY8PbS/JHLesIxN+PHz/mfahZsybMnTsXvv/+ewAATczh2LFjkJycDL6+vtC5c2deH8UBqA/Pnz/n+mPr1q0AkMsPYsyAxp1sRbXtp6eDRCC/0Gg06o6fOYiOjtbUwxgDLy8v3fdLlSoFVapU4X1QFIW3rVSpUtx31wMfHx8eL7l69SoA5OI1MjJSssHyQ3fiO/mhT0t4Uftdet927txZEw8SoWTJkpp7Iv8VLFiQ3xftvps3b4KiKNC+fXtARMm2Mhdr0ItnEa8AmOJaISEhGjrq1KkTvHnzBhwcHPg90RYhEMeJ/LkKFSpo/FG1/iC7msBoNIKTk5Nu7A0g14ahi8Df35/fU8dLGGO8LOJr9fiTLlDb3fXq1YOdO3fy/j969IjbCZ07d+Y8KgIiwpkzZ2DQoEGcz+7evQsrV67k74wYMQLWr18PiAhXr17l/oCzs7MmBiO2T7Q3RCAfkPiQ6I4xZpEG6R0AgD///BMAcuUy+Tp69j6A7Ovo8ZPaTwQAWLlyJXTo0AHc3d05T2VlZQEiws6dOyEqKgpGjRoFHTp0kMbxjz/+0PgXACZ6adu2LVy8eFHCrx4YDAYNv1Ib7Ozs+DNfX18ICQnhuBTlG9lb27Ztg4MHD0r6wGg0cl9ehPT0dM09Nzc3jX8eEBAgxVbV9inhk/ou2tgAuXFNel+UH3pAfvWNGzcku4d8fbKDX758yeu0JDvV8jU/MjavMvXKjomJkezTY8eOAYAcQ1DrKTWQvVG1alWpbj379KOPPpJoi+StOk4DoI2fAmjtOnPtAZBlKsk0X19fzTM/Pz/w9fXV+CrES2S3IiJ89913/DnJVr36RLlq7lLL1veBD5Nv/xKoU6cOICLMnz9fcuDc3d25YHN3d4d9+/ZprtDQUFAUBWrWrAk9e/YExhiULFkS3rx5A05OTqAoCuzduxfmz58PjDFQFIV/k5SUBFlZWZCVlQWLFi3SCGVbW1s4ceIE7Nq1SwrgT5o0Ca5du6YJWDk4OMDkyZNhxYoV0n0rKytARJg9eza8fv2aBwRfv37N3/nxxx81eCEi/+OPPyAgIABsbGyAMQbFixfXMFxaWhr07t0bgoODJWVgZWXFFaqbmxu4u7sDAHCDx8HBARhjGoGxdu1aTXtIAJw+fZpPQOhNJp0+fZoLM9EBIaPBzc0NAEwKgpQF/SXIyMiQAgkkvEi4urq6SgKTlNvz5895wBTApDTOnDkDAADe3t5cMZLzS7+NRiPv32+//QYXL16UnLHChQtL9ZETRPdE/FF9BQoU4MG1ihUrwu3btyEpKUnqJ2MM+vbty2lMURQoXrw4KIoCJUuW5PdSU1Ol78iQFesjMBgMcO/ePd62oKAgTnNpaWkAYJoUofrFfgDkOjkENMZi8JPoSIQCBQpwR6xevXr8/tOnT2H//v086KynaMPCwjTlEZQtWxZmzpwJ9+7dg02bNkG9evV4m3JycmDHjh2a8kg5qh0JHx8f6NevH6SlpcGJEyf4c2pTWloajBgxAsaPHw9nz56FgIAAYIxx3mOMcYNHURRo0KABICL069cPGGMQExMDiAhhYWGgKApMmTIFEBGWLFnCx9LDw4PjXc9xu3fvHv//4MGD0jPR8DY3EUFBBpIvpGTT0tLA1dUVGGNgNBrh7Nmz4OnpCYwxyRBOS0uDvn37QkREBC9zyZIl0LlzZ75gQVEUaNq0KZfDCxYs0PRJURQYPnw49OvXT9PGtWvXwvHjxwHAJFfU9EnyiWSgi4sL//bChQuwZs0a3b7T5LyHhweXg3Z2dhAQEAD9+vWDHj16aL6hPpP8JCft+PHjEBAQIPVb/Eb9HQBAcnIyICIsWrSIL54ICQkBb29v6dvHjx/DzJkzuUNF8o3k7uzZszm9AeTKF6LrwMBATnukV4oWLQo1a9aU+ta3b19dPIkTt+RYieMdGxsLx44dgxYtWgCAic+ioqJ4e8hRpHEQjcPJkyfD0aNHoUSJEvDy5UuJxqdOnQp//PEH/y0+27Jli25bAXIXgyxdulS6L34v4pigffv2molakT9INwJog2xWVlbceXn48CHnPXJkS5UqBYwxsLOzg02bNmnqfvXqFQwdOpTLJpJZNAmkdpwoqEzg5uamq+P0Jp5v374NACY+ady4MaeLR48eQWZmJjDGwNfXl+s7CjaK/A4g64Fvv/0WAEwBYGo7GfQ5OTnAGIOgoCAuQxVF4ZMujx8/5uXfu3cPjEYjrFixQirfysoKPv74Y0BEWLp0qbQQiIKCI0eOhLdv30J2djZMmTJFV16q9Z+IY1EXi+Dj4wOpqanAGANra2tukxAORL729fWV2n3ixAmOx+zsbDh//rwmAPn27VuNTqK2iLLsl19+0bRdDeJCBNGeuHnzJrRo0QIYY1C0aFHo0qWLZG/l5OTAs2fP4P79+1CqVClQFAUaN26ssV3V9AIA3F4mvJAzR/xOk0NktxUoUACWLl0KjDGoUKGCpP8RUbOIRASDwcCD68+fP5faI+JQHPvly5dzOf348WNJFh49epTXC2DCN2MMSpQoAUePHoXIyEheF9H14cOHAQBgw4YNAJArz0QQFwdScDE7O5vrHIBcGUT6q0CBAjyYL9pa3bt3l4K3oqw3Go2wfft2XVzRfaJnCn4R9O7dG65cuSLhTAya0IRmSEgIpyWxLnEcGGPQtWtX6NatGy/j+vXrkJiYCDdv3uRyntqituHNwfnz5/N8R734BiCXp0UdSLKYaEjkabVtd+XKFXjx4oWEj/nz5+vW7+fnB5s2bYLvvvtOM8GNiFw26oFe8IVkO9EbLcQQaZoCgCI0bdqU///27Vuug8QJQdKb9Iz0A41HdHQ0vH37FhhjULp0aY2evHbtmq4NM3/+fPjzzz91J7569uwJ7du3B8YYlClTBoxGo2TzGo1GSEpKAkVRwGAwgKIo0LVrV4nOAbS2qpOTk+4i04cPH/L/GWNchk6aNAkAZD9OHU8gm0EMqpHcFeUZjQ0tOKIJZ/WCLXFRQE5OjqYP3t7e3KdWjy8iwpMnT2D27Nm8zVu3boU///yT/6bgIgDAokWL4NGjR5wGRR6jcSdbMTY2ltN8UlISXLt2DRo0aAAAJltl4MCBfDJXDePGjePxkq1bt/LFmWrQk+VkN5JN4O/vL/kAPXv2hH379sHo0aOBMQYBAQHg7e3NYynffvuthi4IDAYDly80gU30ePjwYe5TirKV/CsaT0VRICYmBhRFgf79+3PbYurUqfw9RVGgdu3acP36da4bGWNw7do1sLe3B0VRYOPGjdCxY0cAMMUErl27xq/r16/DqFGjNO1PS0uDunXrcp5U91FvcZzIf2LAV5xAmT17NgAAp5O7d+9qYg3k4xOULVsWrl27JsUarl27xhfnUFyLeIrgyJEjkJSUxH0zGhc1qMcJQJ8/AHJjZaI+FsHGxgbOnz/P2yrijWwYuqgvu3fv5vf0FpNR/eZ8R3EBmxqqVq3KF1YB5Aa4SfdTG4KCgkBRFIiNjeVxP4Lu3bvDlStXeH8iIyPh1q1bAGCSb8S7Fy9e5OWKuodwLtobaihatCgsWbJEuoeI8Ntvv+m+r7ZRyW5Rj5lo75vzdag8cWGIKJcJYmNjYfbs2XDv3j1Yv3491K9fnz/Lzs6Gbdu2waBBg2D9+vV8cQ1AbhxV9C8IaII4JiZGkj3e3t5S7MZoNEr0ZG1tzX+TzU5xC6Ijej506FC+4Kl48eLSxKEIrVq10izkFBd+0GRtkSJFICgoSHqPNoNQ+0VZJ+KT2k+LWQkXZFdQfeRzmFsoIMZiAWS7x9/fX7IzqJz69evD77//rrFbFUWBw4cPc/lKl7goY/fu3ZJ8VfMu6VwbGxtwcXEBRVFg586dMG/ePN4nRIQhQ4ZAVlYWdO3aFc6fP88XcIhjoW67Hjg5OcHOnTslf2Xq1Km6PrbIuxQXUG9cAABd3tWbpAsJCZFiA+PHj5dijSJehg8fzvlPlNf37t3ji/sYY1C/fn1O0yKkpKRwvV2xYkW4du0a/PDDD5KdQvogr0tPtr4z4Af4V8DNmzd56rtWrVphTk4Of0aHIDLG8PLly5pvP/nkE2TMlPuVUroMGzYMEZHnUKb8++z/bZWkb7y8vHg54nMx1UlUVBTu2rWLb9NkzHTeTlZWltmDB7/44gsE0KYsYMyUk54OaxfzXTs7O+Mvv/wilUOHbZYsWRKXLFnCD1ufNGkSzwVO7aKUK4qiYJEiRfizc+fOYYMGDZAxU3oeyh2/evVqRESeqzk4OBj79OkjbUGl83Cof7RFOSoqCrdu3YqMmfK/I+Zuof3111+l1BiU9x8gN9Xjjz/+iIyZUoRQGjPKXUzpp/z8/LB58+b82+nTpyNjjJ+pQWNEKaGqV6+OjJnSbqWnp/PvihQpws9TatSoEU85xBjjZ3hQ/ymfsq+vL8chAOC8efNw7NixEm4SEhKkLdabNm1CRMTMzEx+lkajRo0kWqPUmNQ/KsvR0ZGn0lEUhZ/BJPZPPB9E+X/pjNT1Ef1v2rRJOivjm2++kc5tS09Px8OHD3MaVJ9BJp4pRLhWFEVKS1q7dm1p3M+dO8f74O/vj9nZ2VI6nUqVKuHBgwelegCAnxtAKV7EtJPHjh0zKzMeP36MvXv31hyE/Msvv+DRo0f5eR1169aV8KZOh0S4trKywvLly0upQkX54uXlhdu2bcNPPvlE6pefnx9u3LiRp+mjs5REWrp7964kK0qXLo0AphzPYtvoOR2mqyi5KcGoj3RWBSJyeiIcUP8oZaO3t7fEtxEREVz20DOSBytXrpT6pygKl2MApnPW6OBcOgPD1dUVz549i4ioSY9JW+7d3NywcOHCGjmoKApPudC8eXMNfVI6hZiYGExPT5fyhsfGxuqm3UBErFSpEiqKgrVr1+apwSjdyJ9//snTk4ntqVWrljTulJrLz88Pr127xtP2Up5sRVHw22+/xeTkZA29ZGVlYbFixTgtM2bK59+jRw8JP8Rz9N24ceN4OZROQVEUnoZs06ZNEl337NkTHz16xFOV1K5dm6c4YaqUB+pzZNLT03Vz7NN5eyJUqlQJGWOaMxSGDRvGD2pX/l96ErUuzMnJwQkTJmhSv9rZ2fFzJdU0o04VQakT+vbty9MVi+MeGBjIc5GLabbE+ipVqsTlnaIo/Oy0devW8bNzFEXBnJwcSc+fO3eOf+fl5YWRkZGoKLkpN0XdIcrCAQMGYHx8vEbWderUCRERf/75Z2SMac7nUMumWrVqSXRKafbozBOR7r/88ktkzJS2Lz09naesLFWqFE8DlJSUJJXZsWNHid+p72LKVLKfSAcsXbpUo+OSk5ORMYZxcXE4depUTr+MMS4H1bggOvnrr794mjBKbefr6yudgxQQEIBxcXEaeqFUtU2bNuU4o2eLFi3StFMco/Pnz/Nx8PPzw9atW/NvS5YsKfF17dq1JXsuMTGRp/ylc2KILwl34vkvQ4cOldpC8t8c36nHVmynqI9E/qN0g99++61kE1IdonyZNGmSpI9EeomLi+PnYSmK6TwwaiudWyLWR/Ybnf1G9JKdnS2lW4yLi+O8JPIo9ZPSPZL9FhwczN+hc1HoL4ApvSOlKvfx8ZFkoZjST1FM5xszZkp99Msvv+APP/ygkRP29vY4cuRIbmOL50OQLE1MTESj0YiIyM8TW7NmjWSnkl1GaX/27t3L7SJfX1+pbcnJyTxlnKIouGrVKv6MdIcodxkzpZTZuHGjZGshIsc10QmlrSS9L/IeYwzbtGnD04RRmYimtJvEW+PHj+f2AdVFqZxtbW1xwIABuHr1am7HiLYb2XUEYsofR0dHjW1nieZFnlbrQEqHS/a3+J05207kT8YY9ujRQ5MmimDjxo3SuyIexe/EZ5QeUwSynb7//ntEzE2d6efnJ6UpTEpKkmhC9GWKFi2K3333naQDRL1J6aP9/Pws8ibJAiqX0hTp2TChoaH8TGkaZ7G/jDEuC0jW+/j48P8VJfdMUFdXV56+inhT1MWIuf6MeuwdHBz4WY6KYkozRs9atGgh6WIxnlCyZElJj1Hd3t7e/ExJkmd0XENgYCBu375diifQe2lpafycF9JN6j6I/q2Ip82bN2OfPn24TV+mTBn+LDQ0FOfMmcP7cPbsWf6tv78/Tx+mHvcqVarwtq1du5a3LSQkRGr3vHnzOC3+/vvv2LNnT+kMdEVRuK+5detWrh/0dPeiRYukGA3pbSqH9AelwaR4CZ35WqlSJSkOQam93dzcpPrIBlPLutatWyNjJv+DxjQoKEjyY0VdwVjuucYk6168eIFhYWGcPglniKiJ65Dt06VLF42fJ4KIM9HnATCl8xP1nyhD1CDyn4hXohtKRTdq1CieGtzJyYnbBp6enhxHjMnHoOjJOrLf1HEttZ4WfW06roRsKVGm+fv783d79Oih8UfVQGf5qnEj2u1qXIg2k1p3iGMhfrNu3TrJZhLP9FQUhZ/PRvJTXe7+/fsluwrAdMbU6dOn+buiH3vr1i3JD2CMYXh4OE8R3a5dO56mnPiZxoHO9ixRogR/TjqO+EXPD6BxUPOsOsZE/RPtaADTeVeIKMk0S3a0nq9DfEk4I3uVnqv9HETTme7EgzTeYjsVxZSCfsaMGZJ/IeJWpBe65+XlhY8fP5a+E/1HdZn0XXJyskYWqP2qvXv3cnmupl2SZdQH9TmUJJPFuADJO4qlhIaGSrLu7NmzHJ8k3yiOopYnNH4UKyG7lo63oDZQnIXOORXh5cuX0hmjdNnZ2UnHYogyguxIEejceBp7Eafm7L7AwEAsW7YsKkrucU1z5syR8BcREcFT5f/8888aGVK/fn188+YNb4c67aQI2dnZUsykQoUKUswgPT1d4l2KG4j1kc1LMSiRdz08PPDu3btSnWIZiqJwXa7ma3P+trl5iIcPH+K0adPMnhNHRwaQPBDPJ88PmJv3eBf4MPn2LwKaXCGDee3atfjq1Svp4HcHBwf8+uuvcceOHfj777/j6dOnMSUlRSIsPz8/fPLkCSIifvbZZ1yQigQjKuCrV68iokxQFKAWiVUUNBSQGT9+vESEjx49wo4dO2qUghhsofIAQDrUmARko0aNcNasWbh161b86quvNEI9ODgYN27cyAVufHw8ent7o7W1NbZs2RIrVqzI+/3RRx9JB2lOmDABGzdujIqi8HOO6HwpyvU8bNgwXYZdu3athKOAgAAEACxdujSeOXNGI4gBTE6dqHTKlSuHL168kIzw4sWLS3itUKECMmYKuu/cuZN/SwZkYGAgP1wWALBWrVpc0QAAN6BXrVqlOTS0d+/e/Lwdukd5fIODg7FDhw66gqpnz55ckKnHgxyW1NRUPHv2LA/UMcZw9OjRUhCH+lm2bFl88OCBFGAU83N/9dVXvD+kaMU6CVddu3blE7TkyDImnyVkZ2eHjx8/xjVr1vD3wsPD8bPPPkMAk2MnOmHqehBNSkLd79mzZyNirmG0Z88eHDBgAAKYDPMbN25gYmKipJj1DlRnzJTvnOhQNCRsbGywU6dOePz4ccnZEyE7O5sfcEp4pMCVm5ubFIxUG6nEsxQcUBTTxFfbtm0xPj4eEVFXVjx79kxywKlP/v7++PjxY0RE3LlzJ//O09OT0wXJMfrO3MHGegdNAwCWKFECW7VqhbVr1+Y0Igbezp07x89Wohz7hw8f5geAkxEcHR2NiKiRB9S/+fPn44wZM3ibyMA0GAxSnnE7OztMTk7GxMREDe3otR8ANHzZsmVLHDlypESfNNlcqlQpPtmnLpMMdkUxnU/Vpk0b/nv27Nl80mHChAk4btw4bhCLQbhOnTrhw4cP8enTpzh79mysWLEipqWlcQfXzc0NW7RogQDAZZVIR+J3iIj379/XnJGSkJCAI0eOlM4VIdkSFBSEPXv2xM6dO2v4PCoqihugMTExPEjm5uaGN2/exOfPn+Po0aM5HdAVEhKC3377ra7OIZ4HAM0B8cOHD8djx47h8+fP8fnz5xp8Ll68WHMgO7WTHFc9g+z69esS/Zu7CDdOTk5YsGBBjIiI4G0ko5cxxg+p1rvMlRsYGMjbV7duXVQU09lthDv1BNru3bsxKyuL03rx4sWxRo0aqCgKTp48WVd30nmar1+/RkTECxcuYOfOnfkiG5J1pJPFsyIYY3yS9PXr15iWlsbz2NNYEd+Ik8/nzp3DZcuWcfk1ZMgQRDSdB0kTl3T17duXjxPdK1CgACYmJkrBInGi28vLC2/fvo1NmzZFRTEd4F27dm1e/4oVK/hCHLKLrl69ihcuXMCBAwei0WiUdKB49enTB5cvX45Tp05Fe3t73i5HR0esWLGi7jdi3v3AwEAuS/38/KRv2rVrp2mnOL6BgYFcFtrY2EiBXisrK2zYsCHWqlULPT09Jd1M9EjtILmakpIiybWJEydK7RZ5jWwnKlNRFCxatCiOHz+eB9NEGUM0WLRoUezatatG70RFRXGay87O5vyhKAo6OTmhr68vLl++nB+6LtpvAICHDh3Cb775RmojgOnsvc2bN/ND0Ony8PDASZMm4dSpUzkOqS3x8fFcVzdu3Fjqp6j/qR1t27bFFy9e4A8//ICMmYJTWVlZ/BwxAJB4VI/Xvb29ccaMGdyZFa9ixYphdnY2Tp48GRXFdA4d2aTh4eEYFRWl+cbd3V0TNKOzMJKSkvDo0aNYp04dVBRFsuEBALds2YKIiPHx8agopknPCRMmcLtv48aNGtkpyk31GSKVK1fm/RXtFOLXuLg4REQ+YUHPKfDNGOO+lVguyVLxvq2trSTfqSyisStXruDz58/5RL9Ynr+/P44bN0665+LigvHx8ZiYmIhFixaV8Km27ajtZcqUwcTERD4uok6MjY3V6EA605LOwhUX7Viy7UR7RVEUrFGjBm/D/fv3cd26dfjRRx/x/vv7+/NJW/FSy1gAwLJly2p0YJUqVVBRFJwxY4akO0qWLImVKlWScCOOJSJi3759uf6j+sj2YYxxW4VoMSAgwCJvvn79WpqQLVSoEDZu3BhjYmLw+fPniIh45swZ9PT05Dafub4aDAZs2rQp9uzZUzdQV6RIEamukSNHcvsDALhOHTt2LF68eFGS2cuXL8eqVatKuKG/ly5dkvwWUS6PHTsWq1SpIrWjUKFCePv2bV0bwWAwoL+/v+5Zbb6+vvjkyRPeJpEfAUwLNcieGDt2LCKi5N/SdyVKlMCqVatiYmIiJiYmYmxsLJ/IFgOpJOd++eUX7NGjh6Y9om4Wr9DQUJwxYwYmJSVJ+FLLZBFycnJw586dmJCQIOnVpKQkPqml568pimkiJigoiMtFeh4cHMx9bJLXZBfQ4qVBgwZxv0PUVbTokHiSeFqUn4jIF8ORPCf5o57wEnG1a9cuSRbQM8aYNFH84MEDKc5x//59LFu2rNR/0c8TQfwuOzsb69WrJ7WHZJmaBr/88kspprVp0ybNt87OzjzOUaNGDT4xTL6fq6urRo4wZlrsLS4cZoxJsg4RNfYbxbVoHPTONAsLC5PogeSk+r0SJUpo/FE1TJ48mcs9tT6mM7T1aJBsJrXuSExM1Ex2AAA/d5P0WeXKlVFRFOzUqRNev36dT37HxMRItiLZYSSzY2Ji+EQ90R7hSoyziDRWpkwZvti0d+/efBKH+FVcqCT2s02bNvx3x44deXyR4j/0/+jRo3Hp0qW4dOlSbNasGQKANFkKIMeYevXqxe937dqV23guLi549+5dLtN69uypa0dfv34dr1+/zs+vvHDhglQXxfIGDBiAjDEp1qU3+Ub+xZdffonnz5/Hvn378gkh9ThQfQMHDpRoQx1jou8obqP+Tq9MWqxtZ2eHjx490kw0/PTTT5z3nJycMDU1Fbdv346rVq3CUqVK6Y7h1KlTpTMSRZl848YNdHV1RUUxndvbsWNH7N69OwKYJoBEWWdnZ8cXk6gXzNNVpUoVPHr0qK7/RPJWUUyLZa9fv8590UqVKuny5syZMxEANLoxPDxcox9E31gEWiSnKAreuHFDd/JNbfc5OzvzhdJTpkyR5CtjTIrFNG/enMfp1L5KoUKF+Lnv4uRb48aNsVWrVtIl8jQASOf00QIM4l3qc3h4uC6PAYDkUzJmWhTRq1cvnDVrFvbq1UuKA1KMCwB4HFdRTOd+5uVvW5qHUBTTRLGfn58kW+Pi4vjEL42lXuxGDz5Mvv0fhBEjRkjK3GAwSEECYnDxEpWyo6MjHjp0iJe3YMECLkgpCK8oCp9dJ2WJqCUoCjyqBRitSFLX7e/vrwmCAgC2bdsWERFXrlxp1nAmphX7bs6YoWf0u1atWti/f3/+beHChfmKOlHYe3t745MnT/jht3Xq1EFExCZNmqCimA6OJRg1alSe9ebn0mu/s7MzDwRHRkZKASsXFxds0KABF6iIyHfEqQ0v+l2vXj3NhJqfn59mp8b79kEci5CQEL76O7/fqt9NSEjAR48eISLi8+fPdVcnUBtpEik/9Zl7Z/ny5YiIaDQaJWdQTR96QXUArQEHYHKwKNgn0qx6jOiiiRRR2VK9agdfNNLEcpydnbFkyZJYv359/PTTTzElJQU//fRTrF+/PjdI1DicPn06N4bpnp7hd/ToUWlXE13u7u7cyQAAjIiIwOLFi/OVImJ93t7emtXcRLt6dGCJx991rEXcnzt3Dtu1a4eMMWzcuDFvCxlZ9K21tTV+9NFHWLFiRWSMYbFixXDx4sU4d+5cnDRpEn799ddS8J/qpdU9tOtOj6ecnZ2xcuXK0q7Xd+WTd73ENri5uWFqaqo0dlSHwWDgesEcPSAizps3T8N/JLcYY9iuXTscMmQI9u/fH7t06YKNGzfmzrE5Xvi7faTyPDw8+KpNc7RlY2MjGYJ/5zIYDNIqUfGytrbG+fPnS06nOVi5cqVugOtdrlKlSuG9e/ewZcuWmovGOzQ0FL/44gtdhxYApGAKXQMHDpTkGdFFo0aNUFEUbNOmDdezn332mVndqQd0qLq4mwrAtBhFLQ/07Bu1DhDpUDxk3MHBARcvXozjxo3DOnXqaCZT3lf/GQwGyXah9pQsWRKzs7MRMXeBBtURGRmJlSpVwpiYGG7b6JUtXmqeyQ8vWOIpwp/YTj18Ulnm2saYvDowP20RV0frvUv/0045PTx4eXlhcHCwtNNa3U6aFBHh0aNHfGf+Py1/1LjR400CsjVDQ0M1el/cRefs7MwD9J6enjxIpsbV+7RfUUwTUT4+PnxCKi86U+M5v/SYkJDA+56amoqMmYIa5cqVQ0VRsFevXoiImsAqXTExMRq6EcdQtFPE5+7u7lzWeXl56dIy3RPpPz/4NBgMGBwcjGXKlOG2jx4tVatWDSdNmpQvmhftQBr//I4nY6ad/tu2bUPE3ImOwMBANBqN2KJFC8l/MGfbiYfRW6Jv8p3ItssPzqysrDAzM1OjAxTFtCtI1CspKSn8Ga36F/FDsHv3bmn3gCV6pT5Y4s3Lly9jZGSkBi80Hn5+fujn52dRjom8YU63qGnv1q1bUkCZntHkop7MbtKkicVy/5OXnZ0dBgUFaXBNC6lSUlJw6NChyBjDJk2aICJisWLFUFFMk0xquad3lStXDiMjI3Vx+i6+pt776nE3B8+ePZOyvdDiP7GdHTp00G2/WN/EiRP54rMGDRpIdgFjpknzUqVKSZN9jJl2BU+bNk0ql3haLT8Rc/0PSzygJ3P0cCcuTCtWrJgUUFV/V6BAAbMZWfSCkl9//fU7yTZL9sm79BcAsG7duvjq1Su8fv26xJt68kU9TvmhN0ttEd9r2bIlMib7oyLUrFkTFcWUcYRic5Zwo5Zb78LPZDNfuXKFTwxFRkYiImp0B9VBvoBo79+6dUuzWBLAZAPo3Rd1yZ07d/iEMj339/eX/FW61BMChANfX1/866+/dOW3Wo6oF7uqx+/w4cNYp04d/ttgMEg8oSeT9YDqGzdunGbSlDKYAehPvtFCX2dnZ3z16hUiIs8yoB4HvTppQZBa1ipKboxX7zt1mffv3+e7xPr376/L06SL1bi3xAPiM7VM3rJli+QH0HteXl5Yv359rhv+rr6rXr06pzsvLy/MzMyUdJUeEM2Tj22OL+l/8o1F2LZtG+/b9u3bdSffLMm8mjVrIqIsX7OysnDUqFF8IpQWZal9avqbnJws2afmfG1LckPkXbpvKcaiZ5Pp8amiKNIOWLWc1+M/c/Ka/G26T3LCaDRqZCv9L27gyA98mHz7Pwp79uzBiIgIs8woMor43MPDA0eOHCmVJQrSrl278pXpr169wqpVq2LVqlVx/vz5iIh45coVXLJkCS5ZsgQRTYK/X79+us5wfgSdwWDA/v37cwWCiPjkyRNpC25efROFmiiMGGNcWLu7u2NGRoY0saJuo6OjI+7duxcREQ8cOIABAQFYuHBhvHPnDl81Sf0mWLBgQb4NLPVla2uLZcuW5TguX768FDAUFaOIrw0bNvAg77Rp0xARMSMjg+88Eb+n1QHu7u5oNBql1ZRq/Lm5ufGV9e+iwMRyKlasiJcuXcKcnBycNGlSnoFtdV0ODg7YsmVLzQrEZ8+eaVZrUH0///wzfvHFF3k6wObqnzhxolTX06dPsVatWhoBHBYWhj/99BPWrFkzz3Lt7e3xzJkzvEw9Z0HtRISHh0uTP6LSPXz4MLZq1UqiA3qPaFr8ztwltpGCvdbW1li+fHneNnMGHCLirVu3OK7V7ddThGJ9pUuXxlu3bmnKzMjIwPbt22vKrFixIi5atEhK7/a+l4eHB19VBQD4888/81SF48ePl9pDBq65vlgyJh0cHHDu3LlSeaNHj9ZMRCYkJOD9+/f5O1OnTjWL05CQEL5yNj94YMy0Sjgv3tNrP2NMWgVlyaAn2L59u2Yy2Byu1HK7UKFCuHnzZhwxYgTfdfV3x1qPt+hydnbmQe781mVjY4Pt2rWTAj951amWBYSH/K6eevLkie7kV36uWrVq4du3b3XLffbsmUZ3XLx4UQqcmRvHhg0b4s2bNzV0cefOHXRzc0MnJydcu3YtT/nl6+uL9+7dM6s71UATk0FBQdIuMDGNl+is0mVlZYXffvut2dSNev3S49+wsDAua/JTjoeHB44fP56n69P7pkiRIhxniKYUJbRCT68djDHN4iNLcpZ+izhSt4HsC7X+tNROciI/++wzPjlZpkwZfj8uLo47ctSPFi1a4K1btyRZbo5HaHeZJTyLzypXrox9+vThu3LV36nxItJOvXr18OXLl7o0l5OTgwsXLsQiRYq8k0x4VxlE9woVKqThTdHWpNWvhNN79+7xHZzmbAcXFxecOnWqpLPy0z6aMFLTl5ov9J7plSfStbk64+Pj8enTp7zvmzZt4uUmJiZivXr18PDhw5xXxB0qdOnZMepA6a1bt6QFaXpjoS6HMdOiky+//BKvXbuGP/74Iw4ePBiTk5N54F8MtonlWNJximLa0UNyUKxXvZtGbF98fDzPcPGudFmhQgX87bffOJ4pXbmimFLiqnUA4Uxt2+WHvglnom1HcqJhw4ZmF6wxxvgON7UOCA4Oxnv37vGMGXPmzJGeUco+PV16+/ZtHkhV98GSr6bHm0SH33zzjSR7zMnkvOSA+D6lf9WjywkTJuDp06c1C7vEMtUyGxGxd+/eunX/E/LsXeQcgGmChiYL58yZg9u3b0fGTL7onTt3sHPnzti5c2dMS0vDKlWqcD1l7qpWrRqmpaVxP+/vjIH4rGbNmmbtJT2gHUqi/qM0oUSLJ06cwM6dO2O5cuUwIiICS5Ysic2aNcMqVapgSkoKrlmzhqeCHT16tFm7QGxrQEAA/vrrr/jXX3/h/v37ce7cufzdefPmYf369SX5SSBmPKHyHR0dMTAwkE/Q5EUvevpWrSPEZ7RISw8OHjzI5YMIe/bs0Z2Qye8VFRWla+OYsw2cnJywQ4cOUhu++eYbbNmypeQjiPJFPU56uKIgt52dHYaEhKCbmxt6enpi//79sWfPnhgZGYmRkZHYpk0bPgHLGOMBcrU/imiKu1GMbvHixTw2l5CQoLHdqc1qufUu/PzNN99wm0lchP/9999rdAc9O3funK69/+eff2oWjujFBwgPYWFhePToUd53cRGOJftDfQUGBuKvv/7K22hOBtBvd3d3KT4m0gv1T9ydrIdTPZmsBhFfT548kdJxiwsB9XxumiAmHWGuXEt1ijEmxpgmxpvfMufPn48jR47EKVOmaOLCBA8fPsQ2bdrky68Sn9nb20upEAlOnTrF411qes9rYsiSfKOrevXq+PTpUzx//jwvd+LEiZKuUoOa5p89e4YBAQFmF4gxZvKN1fDgwQP+fOjQoXj69GkcOnQoDh06VNKPlSpV4mVHRkZK+vH58+e68vXy5ctStghxs4CiKPxIETV/hYSE6F5BQUG6Gw5E3n3z5g23/fI77v7+/po4WLly5XicfPr06dLOVHU5efnbokyn/0U5gYiSbBWz8anTjucF5njiXYAhqk7K+wD/CkBE+PHHH2H9+vVw9OhRuHjxonRgJ4DpcNeiRYtC5cqVoWnTplC8eHHdshYsWAD3798HR0dH6N69+zu35fLly9C1a1f4+eef4fXr19LBvnrg4uICn3zyCfTp04cfRKyG/fv3Q6tWrfjBzgCmw17fvHkDdnZ24OzsDEFBQbx/derUgV9++QUOHz4MmZmZEBUVBZ9++ik/LNNgMMDr169hyJAhsHjxYnjy5AkgItja2kLRokVh1apV/GBiETZv3gzff/89AABMmDBBOhhTfP7ixQuoV68enD9/Hm7fvg3Xrl2DW7duQXp6OhgMBkBEcHZ2hhIlSkCLFi3g448/5ocdE7x9+xa+/fZbWLp0KZw7d47f9/T0BCsrKyhUqBAMHz4cli9fDgAA/fr1k9r84MEDOHbsGO9/sWLFICcnBwAAzp49C6VKlQIA06HCz549AysrKwgNDYX69etDy5YtIT09HbZt2wZHjx6F8+fPw5MnT+DGjRvw6tUrMBqN/LDzsLAw+Pzzz+Hjjz+G8+fPS/WpYd68eTB37lw4e/YsGI1GsLKygrCwMAgLCwMXFxcIDAyEIkWKQEJCgllaADAdEtqoUSPYsWMHMMbg7NmzUn3379+HLVu2wJo1a+DUqVPw8uVL3mY9sLe3h1KlSvFDxdXw66+/SrSUlJQE1tbWAGA6jPjrr7+Gv/76i7+vKArY29tDWFgYVKtWDaZOncqf0WHmeUGTJk1gyZIl/LDeqlWrAgDAV199Bbdv34aSJUsCQO6BsIwxyMnJgRMnTsD3338PBw8ehAsXLsCjR480ZXt6ekJWVha4urrCjBkzICEhAXr06AHLli0DxhjH0+LFi6Fx48b8sHY9IFwfPHiQ0/vLly/h7du3nDfFcW3QoAH4+PhY7Lse7QKYDrqfPHkyHD58GB4/fgxZWVlgMBjA1dWV027ZsmWhTZs28Ouvv0plBgQEwJgxY+Dzzz8HgNxxcHZ2hhkzZgCA6TD6uLg4/s2uXbtgxIgRcO/ePXB2doZz585xHtIDg8EAZcqUgUaNGkHbtm3B3d1d8052djYsXrwYTp48CR4eHjBgwAB+cDXB77//DosXL4YLFy5IfFmzZk3Izs6Gffv2Sfh+8OABvHjxAgwGA7i5uUHRokWhWLFikJCQwOmmY8eO8N1330mHOwPIBwrj/zt8PSIiAr7++mto2rQpP8y4atWqwBiDJUuWQHBwsFkczJ07lx+y7uvrK/GFWBcAQFBQECQkJECjRo2gcePGvC6j0QiHDx+WZM/du3fh6dOn8Pz5c0hPT9cc8IuI4OLiAm5ubvDw4UPIyckBW1tb8Pb21qXDqlWrgrW1Nfzxxx9cb1JddCgw6dCUlBSoWrUqNGjQADw8PAARYcaMGTB9+nSuk2xsbMDR0RGcnZ3BwcFBqu/ly5fg5uYGjo6O8PLlSxg7dixkZmZynFiiKYKRI0fC+PHj4eXLl/xeiRIlID09XaMDr1y5AjY2NlCgQAEYM2aMbnn79+/nPCDqjocPH8KwYcNg0aJF0qH3VlZWUK1aNRg2bBiULVuWlzNs2DAAMMkltT7MzMzkB6SnpaVZ1J0ivHz5Eo4fPw4AJlkwbtw4uHTpEj8QmjEGT548gQULFkhyuW3btlC4cGEAAOjbty9Mnz7d7CHSalq0sbGB6tWrQ69evSAxMRGePXum0X/Pnz8Ho9EIdnZ2EBoaChkZGRASEgLjxo0Db29vAADYvn07LFy4EC5cuACPHz8Ge3t7KFKkCGzZsoXrDBE2bNgAS5cuhZMnT8Ljx4/ByckJoqOjoUGDBpCamgpLly7lOmDZsmVw7Ngx3h69sS9dujS8evWKy8+3b9/yQ8WrVKnC66V2/vDDD6AoCpQrVw6aNWsGLVu21G2nOXoBMMm0CxcuQGZmJkREREj64sGDB7Bs2TLYvXs3/PXXX+Dh4QFOTk6STBNxJsq70NBQ+Oqrr+Dy5ct8zIhXRDk4a9YsyMzMBB8fH0hPT4fXr1+DtbU1xMXFQdmyZSV+zw9cvnxZo89ev34N6enpkJ6eDllZWZCTkwOKonB6sLe35zSVmZkJ9+7dg7dv34KNjQ1ERETw8fHx8YHly5eDvb097Nq1S2P3iTBjxgw+9vv27eM24dy5c+HKlSscJ35+ftCgQQPo27cvXL16FQ4ePAhv3ryBQoUKwZkzZyQ9kZmZyW23oKAgaNSoEQwePBisrKx0ZaEejVWuXBkaNGgALi4usGzZMhg2bBjcvHmTH66ek5PD6fro0aPw5MkTfki5vb09lC5dGvbs2SP19c2bN+Dt7Q3p6enQrFkzWL16tQYfGzZsgL59+8KNGzcgJycH3N3dITo6GqKioqBChQrw5MkTmDx5Mjx48EAjV9V2yrVr1+Dp06dgNBrBwcEB7O3twdbWFgoUKABfffVVvuyUPXv2QM2aNQEAIDw8HF69epUv2+ePP/6ADh06wM8//8zHkNq6f/9+6NChgy7NW7LtyGZzd3cHo9HIbbsGDRpo2l25cmW4d+8eeHt7Q2RkJABoeVoPZ2fOnIHs7GxARDAYDLxdX375Zb5su6ysLChfvjy3zcjWrFGjBhQsWBAWLlzI3xV1gKOjI+zatQsAAFq3bg1ubm78WbVq1aBXr16cT65du8bLWLp0KQwdOhRycnKga9euMGXKFHj9+jX4+fnBkydP4MmTJ6AoClSrVg3KlSuXb97Us8Fo3AEArl69CgAA1atXh7CwMMjMzIRnz56Z5aWQkBCNzbtu3Tq4d+8eeHl5cR1OcvL8+fPcdyJ/TU+2zZgxA9auXQuPHz+G6tWrc55+8OABvH79GgBMNprBYODj8fbtW7C1tQWj0Qhv376F9PR0ePz4scZ3UscTMjIyJD2GiBAcHAwpKSkQHh4OP/74Ix+/oKAgTjtU97sC+dqICMnJyXDx4kXev9u3b8P9+/dBURQIDQ2FS5cuQXZ2NhQoUAAyMzN1x6By5coWZbEePHz4ELZs2QJGoxFu374N48aNe2e7bsGCBdC+fXsAADh16hTExsYCQN52gdonI56Ojo6GLVu26Na1d+9eOHDgADx69AiaN28OsbGxvJysrCxo3rw5fPfddwAA0L17d0kfJyQkwPPnz/Pt5124cAFevnwp0e+7ACJCmzZtYPXq1WZtOBHIRu3YsSNMnDgRrKys4PDhw/Dll1/Co0ePwNHREaKiouDVq1dgb28PsbGxEB8fz/nPHIh40RvTdx0nSzB48GA4ceIEnD9/HsLCwjT+KIDJjib5WbduXY0d3bVrV1iwYAG8efNG0scAluWWegzLly8PVatWBTs7O1428fS9e/egcOHCULp0aQDI1R2iL3Do0CFde//58+dQpkwZuHTpEgAAWFtbc5zFxcVBwYIFQVEUTZyF6p81a5aurZidnQ0zZ86E9PR0/j7Z6UePHuXjcOPGjXyNhbe3NyQnJ0sxpo0bNwIAQFhYGKSkpPAxO3v2LFy4cAEMBgOEhoZCgwYNzMpkEUJCQoAxBrt374bw8HDYvHkzzJ49G44cOQIvX74ERATGGDx48EDXXxo8eLBGRwBY9snUz9Q2pjk4cOAAAACUKVMG7O3tLfbLEuj5Veb0YsWKFUFRFIs6YubMmbB27Vq4efMm11UEpLOsra0hNjYWatasCY0aNQLGGOfZe/fuwZs3b8BgMICvry+UL18eGjVqxO06AJP/f/fuXY2NogZL8eHatWvD7t27wWg0cj+BbLxChQq9OyLBFHucO3cuAAAsX76c+3l5wf79+2HQoEFw8uRJrq8QEdq2bQteXl7QsmVLs/apOfj1119hzJgxcPr0abC3t4dTp05x+t+1axeMGTMG7t69C4qiwI0bNyTeLVOmDPTp00dj85qLA1Ifli5dCrdu3QJFUWDfvn3AGINKlSpB8+bNzfLf35HXlmzz/zR8mHz7XwKIyIOUJNRsbGz+x9tx7dq1PJV8aGhovsrKzMyETZs2QUZGBgAAV37/BNy/fx+srKzAw8NDE5T7N8Dbt2/hyZMn4OTk9E7GnDkgAw8AoHnz5mBra/u3y/zfAkajES5fvswFb3h4ODg4OPy3m/VOcOPGDbh+/TpkZWXB7du3Oc3q8URGRoaG98yN908//QTjx4/nPGbJGPu3AxnEpORLliz5t3k7KysLLl26ZFaeRUZG5jvA+9+E9PR03T4EBASAk5PT/4q63oWu/23wxx9/wJQpUzifLV68OF/f/Sd1oBr+bXLyffv+6tUr2LdvH/z888/w6NEj7sip7ZD4+Ph/nPY/wN+H/0ma/98CJ06cgNOnT0N6ejq0aNECChQo8F9tzz81Rr///ju8ePECFEWB8uXLv1cZf/75J19E9Z+mlb9rRx89epTjTJwYzy8+/7fqwEePHsGCBQt4/4YMGfJfbtE/B/8Xfat/Szzh3w7va9d9AMtw7949yMjIgBcvXoCnpycgIhw6dAhOnz4tBVA/+uijfAeg/y/Dv91mosVJAGBxIef7lPtv7nd+4f9KP/4b8G+2if5tdk9e7TFnn/4n4H8Lzf+32vlh8u0DfIAP8AE+wAf4AB/gA3yAD/ABPsAH+AAf4AN8gA/wAT7AB/gAH+ADfIB/CKz+2w34AB/gA3yAD/ABPsAH+AB/H27cuAEXLlyAW7duwatXr+DNmzdgb28PTk5OEBQUBNHR0f/o6tQP8D8LOTk58OjRI3BycgJHR8f/dnM+wAf4AB/gA3yA/9/BB1vrA3yAD/D/B/gg6z7AB/jn4MPk2/9i+DvC8F2+TUtLgxMnTsDDhw/BysoKAgICICEhAQICAqQy37x5Aw8fPgQAgIIFC/L7OTk5cPnyZbN1RUREvHee+H8S1EGtt2/fwp49ezR9r1SpElSoUEH69s8//4Rjx47xc6v+m4pp3759MGLECGCMwd69e//j9Z05cwb2798PT58+BRcXF/D19QVnZ2dwd3eHIkWKgIeHh+abc+fO8XzbgwcP1jz//fff4eLFizzFIOXN/0/Bmzdv4Pbt2xr6DAwM/Fv5sP8pyIsH88KnJchLFkRERICHhwe8efMGnJ2dNeeZ/VsgOzsbXr58qWnn+8rJ9/3OXHD8XYPmhw4dgoULFwJjDLp37/4/yg/vC+/LR/+NCQVz9PK+8L708k/Iuj///BOmTJkCmzdvhjt37gAA8PMF9CAgIAAaNWoE3bp1k/LTv68cedc+5Mem+DsyzRzo0Vl+7Zv3AXO6OD09HTIzM6UzJLdt2wa//PILvH37ltct5tIvXbo0zJkzBzZv3szPwfDz84PatWtDamoq+Pr66qZoym//zNFvRkYGZGdng5WVFdja2kp07e/vD3Z2dmA0Gv/Vtt2+fftg4MCB8Pr1a/jqq6/+68GDfzqQ8T8dGPlPyuubN2+Cr6+vlAYvL/nyLnrn9OnTcPXqVTAYDPx8ufzAn3/+CSNGjIAHDx5AXFwcZGdng6enJ2RmZv5tXOdlsxPvqs+c/Sdt/RcvXsCvv/7K6/Lx8YGIiAjIzs7m/XvfFNx69KKuLyAgAOLi4ngd165dk57ldV5ffuDvlvnfDkDmR5ZboglLz27cuAFHjx6Fe/fuASK+k5189OhRePToEWRmZnJ7yt3dHYoWLQoxMTG6OiCvtvwdW4oxBt7e3hAeHv6P+yx6tpY5QETw9PSE4sWLQ/ny5cHR0RHs7e3BwcGBnyPu6Oj4j+lNc3ICwLzMfv78OT+vy8vLC2xtbf8WzvRsG3Ub16xZI8nzunXrWjwT/u+CJby8Cxw6dAgmTpwIjx49gvr16+vy4PHjx2HOnDnAGOPnSd29exc2bdqkG0tq0qSJdA7bPwVpaWlw7NgxflYfnctLxwPkRWd6Ojc7OxuePHnCj3TRg7x00j/td/1vgf/puFxeNpOenaWG/Mo6SqDn5eUF8fHxUKNGDQgJCckXneVlS2ZlZWl0gLW19XvpiP8tCxfzslPeN2bwd/3f/zTv/id98T179sDAgQMhMzMTBg8e/N/3VfED/CugVatWOG7cOLxz547F965evYqdO3fGoKAgVBRF92KM8cvX1xe7dOmCV65ckb4V32GMab5VFAW9vLzQw8NDKhsAEACQMYZ169bFK1eu8LatWbMGFUVBg8GAz549w4kTJ2LFihXRxsbGbFsVRUEbGxusWLEiTpo0CZ89e2ax/6dPn8Zhw4bhsGHDdHGzdOlSXLp0qXR/+fLlGB8fj56enujm5oYREREYGxuLn3/+Od66dQvv3LmDLVq0QCcnJ94mT09PdHBwQMaY1GfCTYkSJXDDhg3YqlUrdHBw4O+o3xW/9/b25mMhwo0bNzAjI4P/fvDggdk+ihAaGoofffQRrlmzBt++fSuNA7XzfeHatWvYpUsXLFy4MLq7u2NISAj+9NNP/Hl6ejqmpKSgtbW1pu/Uf8JleHg4Dho0CH/55Rfs0qULli5dGgsWLMjfE+H8+fMYHx+vKcff3x+bNWuGSUlJWKxYMSxWrBjWqlULR40ahXfv3n3n/uXk5ODMmTOxfPny6OXlhQaDQcNDVH9AQAC2aNECN2zYgDk5ORbLPXfuHFavXh3DwsIwNDQU/f39MTQ0FMuXL48tWrTAHj164IgRI3DTpk14+fJlPHPmDJ45c8ZsuVu2bMGiRYtqeJOuBg0a4JUrV3TH/O7duzhv3jwcMWIETpw4EVetWoW3b99GRMtyRE236rGwtrbGmJgYbNu2Le7evRsREZ88eYI7duzATZs24fnz5zX9MBqN2LdvXwwICEBXV1csXbo0tmrVCvft24cPHz7Ebdu24aZNm/DcuXPSd48fP8YFCxZg2bJlMSgoCIsUKYIxMTHYsGFDHDZsGA4ZMgSrV6+Ofn5+un0wGAwanOldQUFB2KVLF/zpp5+wc+fOGBAQoJGPerIgKCgI69Wrh+3bt8c2bdpgfHw8Ojo68nJDQ0OxR48e2Lx5c0m+hIaGYrdu3TA1NRVLly6NMTEx+PHHH0s8tmTJEl6fXntXrlyJBw4cwLi4OCxRogQvp2DBgujn54dhYWFYtmxZ7Ny5Mz548CB/jKEDv/76K3777bc4efJk3L59O2ZlZfFnOTk5+O2332JoaCgaDAZdGUj/29jYYExMDM6bN4/Tu57sDQ0NxaFDh+LRo0dxw4YNWK1aNezVq1eeupFAT35mZGTg+PHjsXTp0ujl5YW2trYaftKja4KnT5/i0qVLsU2bNli+fHkMCAhAFxcXtLKyQmtra6mfaloR8UF0duXKFdy4cSMWKlRI046goCBctWqVpl8kk2NjY9HHxweLFi2KP/30Ew4cOJDrWL169XQS1WdjY4ODBg3idYhyhOoT6TM2NhZTU1PxyJEjiIi4YsUK9PLy0pQdGBio2wdRnunZKyTP1G3JC7Zu3Yp9+vTBrl274qxZs/DevXuSbBXpjPDh6uqKXl5eZmVCgwYNcMuWLbhhwwb87rvv8OLFi/miP8RcGvz444+lPqxduxaLFy/O6yhQoAD27t0by5cvr7Gx8qIn9aUoCrq5uWGNGjVwwYIFuH79+jx1B8k7tR7Iqz5L+kEcT2tra7O2nWi/3Lx5EwcOHIjh4eHo7e3N7bS+ffu+l34nID3n6elpVpaq5Wrjxo2xevXqGBUVhcHBwVi0aFHJ1khPT8c2bdqgv78/Wltbo5ubGyYmJuKSJUvybEubNm3Q1dX1nXSS2l4Uy2vevLnZ8tR6PK/yEE22coUKFbitHB0dLfXdnLzu1asXDh8+/J3sM5IvRYoUQR8fH4yMjMQ5c+YgYwzd3NywY8eOuHLlSoyPj9f0LTAwELt3744tWrTAkJAQrufVeDAYDBgSEoItWrTAQYMGYUREhAZHxYsXx/Xr15vFyalTp7Bq1apmeUIP94TrY8eO4ejRozV4GTFiBC5YsAB79eqFXbt2xdatW3PaJEhPT8devXqhl5cXr8fV1RU///xzvHTpEiKal5GPHj3Cp0+fotFoxOfPn+Pz588t9q9OnToa/a3XPx8fH6xatSoOHDgQT548qZG7z549k+r78ccfMSYmRvIB/f39MSIiAq2srDR4c3BwwOLFi6Onp6dmPCMiInDy5MmSryTC/fv3cejQoVimTBkMDQ3luO7Tpw+mpKRIekpd5vHjx3HDhg24ZMkSHDhwIHbq1Am7du2KU6ZMwT179lj0ufX0d6dOnXDKlCnYoUMHrFevHrcRV65cia9fv5banZ6ervEDDh48iK1atcLWrVsjYv79ADVN5OTk4MaNG83S2dWrV7Fjx47o7Oycp7wX7ZeDBw9iamqqpE/N8QNjDP39/XH48OEW/VRzPkl+bKnNmzejv7+/2fZbWVlhsWLFNLbd06dPsU+fPli4cGF0c3NDe3t7tLW1RWtra/Tw8MCwsDCsWLEiVq9eHevVq4eBgYFm22POb8rPpcYZxUQmTJiAo0aNwpiYGHRzc0NbW1t0dHTEyMhIbNeuHZ46dQo3bNiA8fHxaGdnx9vg5uaGcXFxWLt2baxVqxZGR0ejg4ODJLNTUlIwOjpat8152cN6oGfbDB48WKL3fv366fpkBoMB27dvz30LRVEwOjoax44di5cuXcIDBw7gvn378MiRI7hmzRrcsmULzpkzh9P1zJkz8cmTJ5JN8eTJEy4/qR53d3dJflriQTVs2bIFAwICJByJ416vXj3JFwcArF27NtatW1cTAxPH3dvb26L+edd2rl69Gn18fPJFd4qiYNGiRSX7jGIwavlmbW0txUkYY+jp6Yk9evSwyNdpaWk4bNgwXT/dHJ3dv38fR48ejeXLl8egoCAsWLAgVqxYMV/xnsOHD+OgQYOwTJkyWKZMGRw9ejQeOnSIP//tt9+4TR8YGMjpJT09HW/cuIE3btzQLXfv3r3YrFkzbN68+T/iC1iCtLQ0bNKkCYaHh2PBggUxIiICa9Wqhb169cLWrVubjRkgmnSHSKei7BZ9MsYYuru7Y8eOHfHQoUManaP2K9XXu8g3g8GAZcuWlehs165dWKlSJS63SC61b98e+/bti1WrVkUfHx9dXWDJ3xb1I+mIQ4cO6dqtffv2xe+//143bvXkyRPcvn07Nm7cGL29vTVxK6PRiPPnz8ekpCQsXLiw9Cwv0ItjHz58GIsXLy7ZYdQPslNOnz6taw97enpiXFwctzFr1KiBTZs2xTFjxuCrV6907XYfHx8sUKCAWf+jQYMGuGPHDrO8S21zdXXF5ORkszpCLbP04jNk36jpSi8uoAfmYubiPISVlZVFH1Cch7h8+bKu3f6+8WY9+DD59i8BImQrKyusWbMmrlixQmOk/x1hSIFoPWP2XcpQ37OxscE5c+YgYq7SZYxJk3Z6bdULBCqKgh4eHrhgwQKzeFqyZAl/nybayNGjZwaDARFNgfvw8HCLfXJwcOBt/TvG87sqoi5dukhjLyrBtLS0fClpEW+urq7Ytm1b/Pnnn//25Nvu3bv5BIKID0VRcPDgwbh+/fp8GXfi2FpSlg0bNsTnz5/jnTt3MCAgQDJO83JSFEVBR0dHToP5gR9++AEjIyN1FXZefQoPD8ddu3bpljt//ny0s7PLEydqQ8LLy0u3vHHjxuk6oGq+cXNzw5EjR/LfJ06cwJCQELP1R0ZG6sqRd6FhteK3traW7iUlJeG9e/cQETErKwuTkpI0tKSmCbqqVKmC27Ztw+rVq0sKM6+2/F3n95/mc3V7zMlrNT0PHjwYERGnTJmi++37tjs6Ohq3bduWbz55/vw5fvTRR5r2RURE4PHjxzkfvQ9uHBwcpECLJdyI35UsWVLSjQcOHMADBw5IulItPxctWpQvvlTTYXR0NG7ZsgW7dOmCtra2eQaE/ilao3ZMnjyZ90mUyWqcqOWBpXIbNmyIHTp04EEX+qZ9+/aIKOtwsT6xXvomLCzMLF71+qCWZ+b41cnJCY8dO6bRY9euXcMpU6Zg586dsVu3bjhnzhw8f/48VqxYUdNOZ2dnVBQFCxYsiPfv35ec0bxoQN0msdzY2Fjcvn17nrxDNCjiOK/+/xMySE+/5nX9HT3wLpfatstvWxVFwVatWmns4bzA0qT0+9jPiqKgra2tWZ3EGMOkpCR88+aNblss6TJLdrF6glzs2/vYMHrl5WUrM8Z0beW8dJk5+8ycjZkf+s4Pz+rhNK9v9GhsxYoVZiel8jOGIu2Ys+XUthAiYmZmJiYkJJjFj52dHe7cuVOSkXfu3ME2bdqgu7u75BcwxtDLy0s3WLtixQqzC+jM9U/PL6DLxcUFFUVBb29vvHfvnjTpnRfO8tMGRVGwdOnSeP/+fUREXL9+Pbq7u6OnpyenJ/Hdv0tblnjLHF3pyQ3x8vf3l3TI7Nmzua4iEP3cd/EDRL3JGMOYmBhd2mOMcRmSX/yYw1NeY6uWPSkpKRo/1Vxs433qyM83kZGR2KRJk3zzdn7aYGtri+Hh4XxS+X11aX76/5/W1SIPifbw/v37dXWtOdtGURSsVq0atm7dOs8JXgBANzc3/OSTTyS+UQejzdln7u7ueX5Hz52dnaVJCz0e1OufJXtN7Yu/K//07t1bt24R8mpn06ZN33us3d3dceLEiTwGY66demNgZWWFLVu2lPiaMYb169e3qPvEMaHL19fXolyytbXVjfccOXJEdwKe6qCJLLU/Q2NH9En+BsEPP/zAF+2I77+PL0DtsbQYplu3bvm2R9Uxgzt37liMzTHGuE+mLoO+Y4xh+/btJV4uXLgwdujQAadMmYIbNmzAbt26cV3/LrKOMYZOTk5YoECBfMmed7UPLNGqOXtQlCEUt+rfv78uDYr2T2BgoNR/EZft2rVDo9HIx5TslJCQEMzKypL0O6JJvuSnHxRnexec+Pj4oLe3t8Xv8oOb/IxzUFCQRkeoZZY6PjNu3Lh89YXiApZ4TOTd+fPna+YhLPVZzxbPrz/zPvBh8u1fAuLAi44MzaTnJQz79+8vGX2WiLlw4cIYFhaGjJlW6+zcuRN/+uknnDNnjkXiL1eunNmyfX19sUqVKhrCdnJywjJlymBycjJOnDgRZ8+ejYsWLcLZs2fjxIkTsVu3bpiUlMSdROr/8OHDdfEkCi36S7tl1ALtXQLDtra2WKtWLaxcubJF4ccY093hERgYiKVKlcLAwMB81+nn5ye1mS5xV1hoaKjZy5LSMvd9WFiYRTp88OCBtOpUz1gSjRC6zDkx3t7eukpM7fBXrFgRw8LCpLLM0bCjoyNfsSG2ydKkLcGCBQt0naOGDRtiUlJSvhSMwWDARYsWSeXmxTt6tCTeq1atGt+V1qpVK/zoo4+k92n1M/2OiYlBX19ffk8diMtvO0iOdOnSha+KseRgiDiwpMwURcH4+HjMzMzEr7/+WtNnW1tbizymVoTvg9O83hUvvfbkVYal53Z2dnxSgO4VKFAAW7RogRUqVNC8X6BAAanfe/fuxXLlyvHn7dq1wzlz5uCoUaPQ3d39ndqqvvz9/XHYsGH4559/muUTo9GIlStX1g20Msb4ri81Htzd3fNlWIuXlZUVli5dGlu0aIFFihTJF+5JN5I8FndMiobYmDFj8j2eQUFB0uphS/TyLv1zcXHJlzMlGn3EI5cvX7Yok+mKiorCmjVrSs8qVKiA5cqVw5CQEH5fURRs2rQptmrVCuvWrSvJlVq1amHVqlXN0nNe/BYcHIyMMYyIiODygfqglmchISFYsWJFrFWrlll8kB5UFIUHEdXyQdR1evhJTU3F+vXr52vM/Pz8sEWLFpicnKwr28TfTk5OGBISYlY3i3aAHq5sbGzQ0dGR6zwAwNKlS0uLJszZG+pLlDHkEJqjL3M0FBkZiR06dOA78NTvNGrUSGorXa6urlisWDF0cXHJN5+RbfeuvGRvb4+tWrVCPz8/i3ZRaGioJtAn4tDDwwN9fHzQ19eXO6ZTpkyReOHvXCkpKZIsbdOmjcV+Eh6qVKmCa9as4TtmzE2Qq30BKmfKlCk4dOhQtLe3z1N3MsZ4ee9rK7do0UL3uwIFCqC3t7eGRsk+E+VZfuskHHh7e2u+CwsLw4YNG2ro3NXVFQsXLszliFjW559/jpMnT5b4VFEULFeuHD5+/BgRTauT9XhJvESa8ff3xwkTJqCzs7Muz+bXntGzrcxdJJfzkhPW1ta4bNkyiS5Pnz5tdkI4MDAQw8LCpJ1Njo6OGBMTY3a3k9jmlJQUjdwNCgrSfBcZGYlz587F4cOH52uhlThORqMR27ZtK+k2sb68Jv7e9SIZpygKTpkyBXfu3ImLFi3i/Z8yZQomJSWZpV/xsra2xq1btyIicn3z9ddf87Ehv1CP90Q60aOpvGjWEl5LliypK8vVtol4ubm56d63tra2OOFirp303YgRI9DZ2Vljd+bHlvonx91SPeb0qehb1a5dW1dm0f9q365YsWIaW+6fbPO7TDiKvjjJ4NGjR0tyJC0tTfKp4+Li8OOPP8a4uDiLY6IoCt9pqEcX72Jvi+/p0ZWjoyNGR0dz/cEYQx8fH3z69KlZHkRETExMxJIlS+rWWaJECS4LRftYzy8SL4rd6LWzYMGCOHToULx69aquX2aunYimuIHe2AEAtmjRIt8T7YwxtLe3x88//9xsP+zt7c1+nx/5wxhDOzs7LFOmDEZHR3O/611oedasWbzv3333Xb7lXn7oKiwsDIcOHYrTp0+XJjz03n0XX4CugIAA3bhcu3bt8t1/UTZSzODTTz/V9V3V33bt2pV/Z0mGlSpVCo8ePSrR2bBhw6TvXF1dMSkpCbt164YTJ07U1fPvcxkMBixRogTWq1ePx6vfZWzt7e019mlgYCC2aNFCdzGmaGOaoyW9Njg4OGBAQIAmm06/fv04zshO+fzzzyX9rigKHj9+PM++ifymKAq2bdsW58yZg35+fvnGp52dHdaqVQtr1KiheVaiRAnctWsX7tixA7t3766xxd7VVxN3oKlllhif2b9//zuVazAYcMCAARgUFGSWx0JDQ9HNzU36TlEUSf5R1htPT0/08PBAFxcXTEpK0sg1xhj3Z94n3mwJPky+/UuABpR25IiDLBq+xYsX1wjDv/76S0o9k5ycjDNnzsTJkydLAVy61q9fj5GRkagoCn7//fe8HGKKvITNu1zUJtqNZg4yMzNx9erVPIBnZWWl6Sdi/iffFi9eLLVBL6gYHR3N/3d0dMSHDx/qBsYZY7hy5UocNGiQLj66deumGQc9/JkzQNVKQy341Ub+uwpC9VhYguHDh/P3evbsibNnz+b1tWzZUipLrRAsBf307ltqJ7XBXJmOjo74ySefYMuWLfnEk5OTEz58+NBs386dO8eNPFdXV/z4449529U8VLp0aV4XBXbUioy2qd+5c0fXoWnRogXWq1ePp7uh+7a2tti/f38piKEoChYqVAhv3ryp60zr8eW7TE65urpq7kVERKDRaMRHjx6hr68vx2GHDh14HSdPnsRJkybx1XCi89K/f38NfX/yySdYpUoV3r65c+eik5MThoWFYY8ePXh/tm7dKn2rKAoOGzYMv/rqK0neWdqxRPTHGMORI0dKhquHhwd/dubMGezRo4dUn4+PD8bHx0uTwGpa7devHw4bNkyDYz8/P2mFodq4pnKSk5NRUXJX1DHGcObMmdi6dWtNmTNnzsSdO3di8+bNudFLBhBjptXw2dnZWLp0aV05QDxhiafUNKUoClaqVAkXLlyIL168kHhl7dq1fAxDQ0Nx+vTpuH37dpw+fbpmYkH8HxFx8+bNmrYNGTLEbNuoLYGBgdyAzY+cEGVDx44deSoZUY+J38fGxuKCBQvw7NmzePDgQezevbuUMrhAgQL4/PlzPHPmDPbp00cqPz4+HlesWIG7d+/GgQMHSu2oXr067+9nn30mPTMYDLh+/Xp88eIFpqam6vajQ4cO2LBhQ+60i0YepXMTZfLRo0c1eKlatSra29tLqzdFGSr2xdx9NU4tyRq1catuy8iRI3lAplevXrrjbU6+v8/l4uKC69atw3PnzuGgQYP4/fr166PBYMDExERN/6ytrSX+N7fwgDGGderUMctXeelnc/pPURRs1qwZfz88PFyq85NPPuHvW1lZ8Yl3xhjf2UcTq7Qzs0uXLlipUiVNfdHR0digQQPdiXHGGI4ePRp37tzJ2xUfH4+rVq3iv48cOcInBXx9fXnwUFEUXLNmja7dQ3pEfRkMBl0a1rvUTmB+bCNz42TuO0VRcP369dIzcYEDvSe2xWAw4Lhx43Dy5Mm69R08eBAREXfu3Gm2b5988gkWK1ZMakfnzp0lOXzkyBGMj4/nz8Ud7vHx8fjNN9/w38+ePUM/Pz9eXnx8vDTprYeTXbt2aWxlsovEKzY2lv9PtvKdO3fQ3t5eI+sPHz6MiLmTq5SWl+wzkmd6bRJTjuf36tmzJ69P7SCr/Sdqr729PZ4/fx6jo6NRURQe3FEUUxquu3fvSr5AoUKFJH6gv2lpaThixAj+e8aMGfmmwf+JS7RL1fQlThQZDAYub0iPI5oW4aj7991332n4zdnZGbt27crLozSWFLhWFIVP6lhZWWFUVBQv8/Dhw9iiRQvd9hcvXlwXh4piynwi+nA2NjbYpUsX/vzLL7/UlKcopnRK6nLpcnFxwVKlSmn0JX1LbSafUwwkPX36lMtnxpiULnTLli148uRJHoRjzBTUefnypa4vrjf5Zkm+/ROXoijcdlMUBaOioni9NWrU0LWpSIbQN/8EzVNZaruHdou8ePECv/rqK91vFy1ahBs3btT1x8wtWitbtiyuWbMG165di19++aXUZkVR8KuvvtJdeNKxY0e8f/8+Hjx4EDt27Cg9s7W1xf3790t6s2DBglxfN27cWKIltZwXfW2xLRMmTOCLoNXPdu/ejTVr1tTtb6dOnbB+/fpm8a2n48SxcHV15ZNkiqLgt99+y2mVfDZbW1vcsmULv//mzRtNkNvSpI0erbRp08ZidgX6v2DBgli2bFl+n9qqZx+INlBqaioeOHCAZ+AYNWoUz6Zx4MABXR4UcW6ONy3RvfhdgQIFpMluUW8lJCTg/PnzpV1SerKC7ARRTg0bNgwXLFigy0sfffQRn7gnewFAnkBcvXo1du7c+W/zsrlxo2B2p06dENF0fMHu3bs1fFusWDGcOXOmruxRFAUvXbqEL1++1NjxliaAHBwccNq0aWZtNypDPZ7VqlWziIf8+ALm3lcUBU+fPq373uTJk7Fw4cKa+46Ojnjt2jXs1asXMsawevXqaG9vL/G9oih8sskcfTZr1gybNGmiOznr4OCALVq0wF27dqHRaJTkWXBwMK5ZswYzMzM5HYr6gxZlVK9e3WzstHPnzvjNN9+YXXhz+PBhs36JGocAuXYI8VD37t3RYDBwf5VkFz0XFy+Jk+xUpoeHBx93+r59+/a8PoPBwNMhZmZm4rp16zhPWVtb8wXOlI53xYoVkn5XFEWyrStVqsTpXcRXfHy8tBBi6dKluHLlSl2clSlThv8vLjzz9PTEly9f8oUdrq6uGBMTI9lhYixQXIjcs2dP3LVrlyQjRDmamJio0f/jxo3TlVmizSRudlHHXS3JDz0eyo/cscR/oj9D75Cvioj48OFDHDlyJI9R5RVvzgs+TL79S0A0xC5cuID9+vXDggUL6hJM+fLlcc6cOXzVztChQ7nhtHnzZqlcMvLEIKOzszPfMvzrr7/yd9PS0swSZ16EnZ+L8ilbun777Tc+AdekSRPN84kTJ2ocYL3JN3EHxbZt26RnYlCblBIZLKKAK1asGPr6+vKgFiJicHAwL5cC/LGxsZpxWL16Nc+RS4Jl48aNkqDVEw729vZmV6Y5Ojqij48PhoSESCvk/f39MTg4GH18fCwG4G1tbdHNzQ2nTp2K06dP171CQ0ORMdMk7/Tp03kwmTGGjRo1ktoqphIUgx75pQdz76rLoqCBuNNLNFJFJ3fMmDFmeaxDhw7ImCl4eOXKFYkm1Dyk3kWp19YvvvgCERE//fRTfq98+fK8zYimtHh79+7Fdu3aSWW0b9+e7zSj1H6MMa4M84On/OCb+pCVlYUlSpTQPF+3bh3vu7u7O6alpWm2cB84cAA3b97Mzzvw8fFBxhg3Mtzd3bmjbm9vj+vWreN0bmNjgw4ODjh8+HCJlqpVq4aMmVayUnCuZcuW2KlTJ94nSkMTERGhUZSEYzqXsVGjRlzWiRM41Ifx48dL+HJycsKbN2/ixYsXuaNGZYWEhKCiKNi4cWMeHI+JicFJkybx+snRo/5ERkZibGysbgBCL5Bjbiz1njPG8MaNGzhmzBhOo5MmTcLPP/9cqmP37t04bdo0XfmhV77IQw4ODvjpp5/izp070Wg0YsOGDZEx085Z9cQcnRtCZVSuXFmiFxFn5cqVQ0VR+OpftdFsjpYjIiKkVc4kP4sWLWqRzsuXL4+DBw/WPB8yZIhGHhBdi85Kt27d8MCBA5wOqX57e3tcv349HjhwACtVqsRx4+HhIbWxQoUKqCimVDtEt15eXvj48WMcOnSo1F5y+Bs3bowHDhzAhQsXcj6iq1ChQlisWDHujB84cEBzFmDhwoV5/0kmi/rxn9LhemOmt1OR6iVdJL4j0uHfbZdal3p5eeGJEycQEbkjyRjDqKgo/O233zT1DRkyBJ8+fcr1uHo3NvEaAEjBYpG/rK2tMSgoCH19faXL3C4qBwcH6T1/f39UFAXj4uI0Ok8MSNNKQjEQTpNiZcqU4d+pJ+0plaCNjQ1euHDBrB4oU6YMl5+lSpXC169fS3qAVkFHRETgo0eP8M2bNxgXFyfRn5WVFa5fv57fFwOM6nz+4uIFRTEtxpg7d26eNKd32draoqenJw9ukq1rY2PD74mOor+/P7eh6IqJieHPv//+e6l/eqtLFUXhtgb1UeRdLy8vvHv3Lp/0BTDt/Fm2bBnvE9m0GzZs4EFKxhju3r0b161bx68VK1bw1fIU0AgLC8MVK1bwCX3GGJdZFHQ4cOAA9uvXT6Ipf39/CZ/169fX2MriuIvjINrKY8aMkSY6Zs2axRfmtGzZEm/cuMHfJZpljGHfvn2xVKlSUpuo76Q/1DKS/lfvZKIyrK2tsU+fPry+qVOnYrNmzTSONPEfBRU6dOjA09IcPnyYT4woiiKdYRoQEKDhB/pLvgfxh5j9A0DO/EB1U5tFmtSz24mnxMCUv78/+vv7W9wlZmNjg56enhgUFMT1k5OTE2/zp59+im/fvuVl2Nra4uHDh6X+qf2uxo0bI4BpooJ2tEVHR+PGjRvR0dERGTPtpBTlbqFChXDs2LEaHu7cuTM+f/6c+0edO3eWFlL17NkTP/vsM45P+p5W+lM5tWrV4ryuKApu375dSvElLhAhG5Nol2xeAJD4u3r16oiIUsCNMSbtRjQ3+TZ27FipD7t37+Y2QExMDPexmjVrxstOTk7mdNq7d2/+jnoRj62tLfr6+uouoiM6MScnDQYDpyM9+9TFxYXLQdF2MxqN3G6rVq2alJaWzk1Xy3lXV1eJlsWJBX9/f4uLdqgvDg4O6O3tzW2phg0bamw3S7ZUz549+fiTPwtg0o1iWjq1L4CIOHfuXI2NM378eL6jfNKkSdLisx9//FGKs0RERPAsFiEhIXwnCulNWtRGvpRIS6LOEflh165dGB4ejoqi4BdffIHZ2dkSHskW+Oabb6RFxqINVq1aNQ3Ohg4dqolRHDx4UHNkgZ6tZm1tjYsXL8YDBw5gWFgYKoqCn3zyiTRxRZMCos3ftGlTfk/kA8ZM8RYAOWBcvnx5qV4x+KtuU5s2baRnvXv3xl27dpmd2BfxJH5H+NTjFT39Y+63uUvkD+qDXrYfemZjY4OJiYk4btw4nlp3/vz5Eq6rV6/Ov+vUqZOk+0W/hHipc+fOyFhuGjvy4ajeL774gu9eZ8wUqA8JCeGL+8SJGn9/f/7b0rmhoqw+ePAgD8ovX74cEVFaNEcX8Sb1Y+PGjZIc8vHxkeioSJEi+PjxY0kP6C2oX7x4sWS7qf1KMV5K7Rcnaiz5AmrbUm+XlZ4NKk5ENGjQgNeBiHz81LuM+/fvz31Sapu46ERRTItU9u3bhxMnTtTdhceYKWail8VA5IeAgAAsUqSIJM/UIOoPSj8aGBiIiChNbpE+/eKLL7i8dnJy4r4Evde6dWuzfgm1LyYmBg8dOsQXJNK5ePQ8KCgId+zYwXUxxa08PT1x7dq13EZq3LixZGM6Ozvjhg0bsF+/fhracHZ25nGm7t27440bN/Dw4cN4+PBhXLlyJeeH1q1b4+HDh9HNzQ0VxbRb6vDhw9ICXqKhkJAQ3Ri3OJbkRyUlJUk+y5w5czA1NRUZMy26E+0wSieqKKZjIGizwpgxYzAtLU2y2ylLlaurK+7evRtTUlIQwORr0zMrKyseA6RyKX4h8iZjjC++F2WWGL+gviuKaUe1KLPob17ZkBiTN4KQfLWyspL4MS8fkCaIDQYDrlq1StK5ImzdupXzhKV4c17wYfLtXwJqQwzRtOpw7969kiIQhaGdnR0mJydj4cKFUVEUTE1N1ZRLRuPKlSs1DoWimLa8Eqgn3xTFlH954MCBmJycLH3bqVMnHDp0KF+hZWtrazZHrigoRcMmPwbOP3FRPVQ/MSdjDKdOncoPQq1YsaIkUGbOnMkZMj4+HhFRMkho67aLiwsiIk/ZROMgrvagsc3IyJBSpegZcOaMOHoWEBAgBRtEmkFESbj9Jy+xrUQry5cvx02bNvF3XFxcuOKZM2cO/vnnn5pAnFjGsWPH8Pr16xJ+mjVrhoimA1r/J/qlpheRP8l5IOF+7do1Hrz18fHRTFyJNJ/XuOYHz9S+3bt34/Xr1/H69eu4evVq6X0HBwe8evUqf379+nVJFojOjb+/P3eiR44cyeWAuT6Ya5/agcmvI5Kfd9V0NnDgQM1kTmBgIO8fOX30PiJi1apVuXNODidNyFBAg5xD0UEmx3LmzJmIiPwcFvF8EQr40Xfv2r/80qQejeqNgyU6e1d5qyezzY0P4VrE2fLly5ExxoO+Is4oAO/v748pKSlS2XqG59mzZ7n8FOWGXlvF++Hh4RZ1rh5u34fOFUXhwdxNmzbhzZs30dXVFRVFwfHjx3P9QO/SrmIxEJOf8bM0huJYULCN5PKyZcsQIDcNpt6YquVyfseeHPm86L5AgQK4fPlySTZRPSTTSJ7pnUVE7aADsBVFkQK0Li4uuG/fPil4NXXqVM25CwDAD2EnPR0XF4eFChUy2wdyFOzs7LBUqVK8PeKumbzsGnN41+unuPOAdlL98ssvkmw7fvw47tmzh98TJ5KrV6+Ob9684U4EnSlCcpIWORGuaXfz2rVrNXogODgYFUXhQQvE3N2xtEiC7B69AOPp06clu0ecPKhdu7ZUn9pm1ONzPZySPezl5SX1A9F0liG9r7aXEJEHVkqUKKHpnziRWqZMGW4v1qhRQ5Ijp0+f5gFEAFPqIrLd7e3t8cqVK1LgwBy9/FP6Qi1PFMU0ySSmQbO2tuaLT+Li4jTjLo6DaCuLqWv09M679EetP8Q6T58+zVOM04LBOnXqSLtE1fX1798fHz58iM+fP8d58+ZJtCbSE01KKEruIsTRo0draI7Od7A0+Ua7f7y8vDg+DQYDjhw5kq/UDgwM5LbH0qVLERE5PkW6oXampaVpdBXVRzs11ZN2onyxs7PjOxy+++47LFGiBH8uBmv79+9vtn95yf786Cs1PmnCguSuuBigSpUqGnxSOT/88APvA4Ccar1WrVpmZYiiKNijRw+JdkkXizgBMNnwooykRWJqWtObfFNPfFjCzftcYr8UReETSw4ODjhy5EiuB0SaIDrTk4PUzosXL/Lnant3/fr1yJgpgEcTVyQLRDkvtkXcMUOZGkScUTrzihUrmu2bSDfiubEEJUuWNGtL0c7Fnj174vfffy/pxoiICF5n+/btNb4A+QlirKR27dp8Z/nJkyfx5s2bXF9ERERI9LJ27Vo8e/Ys2tnZSfYg6c2TJ08iY7m7CkS8iDpHzQ+LFi1Cxky7TRCRBxIZy53QqlSpEpeVhJeWLVvy8StZsqTEE9euXdONUYg4c3Fx4QHY/NBnfuwfeo8WitGkOn0jZoRhjPGdfoqi8PGqVKmSJgWp+n8Rr+bs0bz4LC9dRedrkn22b98+zTcnT56UaPfly5ca++Zd5YQlXKt5SO2XUDvFcaeFpXSPbDcqj2TMjh07kDEmna937tw5LidoIbgoX0Sc0f2WLVvi1KlTkTHTQm9E5Fm6GGPYunVriTfFujIyMqRjV0S6oXO2RLlMsl6Ui6GhoZJePX36tLQgLCQkROqHeozM+QIU8H/XtJPiIgErKys+gU90RuO3bt067v8CAF+4IeLWEj1Y8jnU3yQnJ3M7S93/4OBgnD59umbnj6g/RFmHiJKNQ4sSgoODefx6zJgxkn8PYIorEx2o/RLqK+kqst2io6PxyJEjmv6reSwv+4XK17Nh9GyCd+Hf/NQn/qVnixcv5vKcdFCRIkUQMddmcnd31/i/tWrVQsaYtBjQko0i0ow5+lDzeF42T15lmuu72ndkTHtMjFgn+Qmir6q2ffR8QFooTvFmtc4VQe0Dvg98mHz7l4CeIUZAht/w4cOxVq1afNWAWug2adIET506pfvtyZMn8eTJkxqCnzhxIn9XPflGq2KKFCmC8+fP1xhpiIi//vorMmaaeRaDZaNGjZLOrNBjxPcxOt7nUhv2tMKDMVNQa1tCaIAAAQAASURBVN26dcgYk86TUBRTDmXa8uzl5cXxSd/Stn5HR0dERPT09ERFMQUREZF/q8YZImrSZjBmSvdDRo05waIn7CnYQCAKmf80jql8Wn1vMBik3ONWVlacjsgInT17tm5fFEXB7Oxszg9U/rp16xDRdEaF2mj9n6AXNX926tSJvysaqb169TI7cZVXe/PTF3O09Ouvv0rvtWnTRlfGkCw4ePCgtJqEAlCULspSH/LC2fvi+136fvjwYczJyZG2/5NCVhRFShfCmCmFEqW5WL9+PU8lWapUKUSUd8msXbtWMhppkm3v3r2cdtWyl3BK34ntbt26NQ4dOpTvUnNwcOC4bN++vXT/XXBkSa6qfyuKaVU26Y6/ww95GVaIKOGM9IMYbCCckU5xcHDg8pPGRe/cGw8PD+zevTueOnWKG9r0rEePHpJuFL9zcHDg34lgySg39zsvHlDLOlokUaFCBa4fqP/Hjh3T4CY/ciIvfqGyzpw5I8ll2r0g7gLQcyred+xpkmfIkCHYvn17s+eAkdH64MEDjWwVbQrRnqhevTrffaZuS3h4OH7++eccj/b29tLh4QcPHtSdfDt9+rSkpykHvKKYFiuNGjXK4tmqFCQSxzQ/dk1+3lEUOV0tBeJFHagoCq5evRpfv37N74kTDeSsUmCFghyiHhADoOT8EO2KeoCCiWIgSZR3ot1jKcColy6Mgqt6wf89e/bgqFGjdFcPm6ND+v3pp59yns/L8aJ+DBo0SNM/0dGbOHEitxf9/Pw09LtlyxYJn/T/Z599JvUxv7z1vpde2dTGnJwcLgvEa/DgwbrjIPKR2HdL9JyXTNVrq2hv0P3s7GwcOHCg9A7tbl26dKlu+YyZJoQbNGiAGzZs4DK5Y8eOmkk7usLCwngwac6cOdLY0VlplibfTp06xWmeVk/TxBelrLOzs+O8R6lsCJ96O4KKFCmCP//8s66MpJ28Ir937dpV4x/SM1dXV+zcuTMmJCRonlHgWN2//Mr+d5V1lJ2F5K64YIBsfRGfYt9fv36NCQkJmnrofBVzk287d+6UaJfkPNkRNN62traSjDx58qTG1hDHQcSZuDDzP8XXYhtev36NPXv25PY70ZmoN9VHN+hNvmVlZfHnanuX6MzW1lYK6In2rp2dnaYtVL46SI+Ym6GGdDSAacetuZgBY6YJ2ilTpuD9+/cREfmCTuqDaEvRhMjWrVvxxYsXkm4U09Hv2LFD4wv4+PigopgmKei7gIAAaXJNrcdev34t0QsiSruQxfuWdr6pdSoA4Pjx4zU6FRGlsdi4cSMyxvguW71gLWW9UfvaejEKEWfW1tZc/uSHlvNr/yhK7kTa4sWLsU6dOhq9SP+PGjWKfyPKz9evX+OqVat0U/IBmHafd+vWDU+ePMm/K1GiBH799ddSajb15ePjg0lJSdi7d29s37691G6a9CRdRWcRz549GxG1vjgAcH0lgtq+Ue82zO9lDtfiOCiKovFLyMamvnz77beSHKSFDYyZzmrSkzEi/ZK8trW1lRa337x5U+JrvXYzxvCPP/6QMhjt3r1b4k01r6xbt07T5+TkZI5fUS6TrF+3bp00oUa7q6lcMf5JfCv6AnqpZ+lSn4P6LrpT7x3RxkBEzfiJ2ZbofbHc9+FLvW9KlCiB3bp1w5SUFC47RR4W7azMzExJf4iyDhEl2UuxUzHGIuoqddpdPb+E7pOuolT2Tk5O+OjRo3z1/V3sF3P0aw7nf4ef1XamiAPiM6qTbB/yDa2trbnfSrqaMm6os4f9HdtO/d7fjV+Y67ue7xgQEIAff/yxRPfqKyoqCqdOncrjDHn5gLRok2xQtc4VQe0Dvg8o8AH+9VCgQAEAAIiKioJdu3bBrVu3YOzYsVCsWDFARP7ed999B6VLl4aYmBiYOHGi9O3Vq1fh6tWrwBgDNzc3YIwBAMCAAQPg9OnTmjoVRYGlS5dCqVKl4MKFC9C+fXupLoKzZ88CAICfnx84OjoCAABjDAYMGADnz5+HY8eOQadOnTTfUVkBAQHAGAPGGDg4OICvry+4ubnxNojtsbGxAYPBoCnL2dkZ3NzcwMHBQfMsOjoaqlSpAlFRUfxejRo1eBvc3NwgLCwMAABevHgBvr6+/D13d3f++8WLFxI+AQCuXbvG+6D3jliWGipXrqzBR7NmzeDevXt87ACAjxP9JTAajfz/sWPHQkBAADRs2BA2btwI2dnZ/FlMTAwMGTIEhg4dCu3atYP4+Hjd9jDGwMPDA6ysrAAAoFixYlC1alUoXLgwf25jYyO156OPPuL/Hz16FKZOnQqOjo6wdOlSXq69vT2ni7t37/I2mYMnT55o7tH4PH36VGqvn58fVK1aFSpXrszfMdc3se2Ojo4aeqFnpUuX1tCLGho2bMj/v3v3LqflkiVLat61sbEBAFOfv/nmG37fYDBAbGwsAAAUKVIEhgwZAsnJyQAA4OrqCra2tgAAYG1tzduvx38AuTxIkJCQoPse0eWdO3dg9uzZ/H52djYYjUbIzMzU/Y76ULx4cc0zxhh06NABhgwZAikpKQAA4ODgAIMHD+bveHp6amjJysoKGGPQunVraNeuHQAAWFlZgaIowBjjOGaMQYMGDaQ6bW1tQVEUmDt3Lq8TESEnJwcAAG7duqVp57NnzwAAIDAwkPPejRs3JLwAABQqVAiuXr0KAABeXl68THd3dwkH6rFwc3Pj31lbW/P7PXr0gCFDhkDPnj2BMQbZ2dng4uICjDGoV6+edJ/67+/vr2l/SkoKx039+vUhJSUFkpKSpHFo1KgRpKSkQMWKFTXf169fn+uOVq1aaZ4TUBtEoL56eHhAkSJFpP4BAERGRkq/RZzR/2r5BZArw0g+vHjxAhhjsHDhQrh16xZ07dpVev/Jkycwffp0KF26NKxevRoaN27Mny1evBjGjRsHt27dgq+//lr67vXr1/w7UTfq0bWVlRUMGTIEDAYDp22RrocMGcLHLzk5WeJpsS8k60jXXL16lesHAuJbLy8vSU4MGTIEWrZsqcFX7dq1YciQIZCamird9/DwAABZJlNZolzevHkz76PYd19fX2CMQeHChaFp06b8+08//RSGDBkiXVSPXn0JCQmAiHDu3DmYO3curF+/HgBAo7MREfbs2QM+Pj4cP3p4IZ4DABg+fDhcvHgRjhw5AqmpqbzPhNtVq1ZBdnY2GAwGePv2LTx8+JDXRfaEGi5fvgwAIOl4GqOiRYvCgAEDuBxzcXEBLy8viY5fvXoFALl0bDQaISIiQneMCF/JyckSPomW9GS2qOOp3tu3bwOASQYiImzcuFH6xs7OTvM96Xz6VtQDrVu3lt43Go0afQIAHIdE1wDA5R2BaGsCmOhaDaLMIsjKytLcI/Dz84MBAwZw2jUYDJpxIDAYDBLO1qxZw3l+69atZusAyJVx0dHR/J5eP0JCQnTtEQKyZ0m/EYjjQsAYg/DwcEhJSYGUlBSoWbMmAOTqN8YYuLu7Q2xsLAQHB0vfBgcHQ2xsLBQsWJCXRd907NiR05doqxAoigLNmzfX3Pf09DSDHROItrLY96CgIEhJSYH69evz8klmkl6qV68eAJjGiGQPAECtWrV09RXBkydPIDExUXMPAKBgwYLAGAMXFxcuawBMY5mVlQXbtm2DTz75hNvDH3/8MVy+fBkmT57M20bw559/Qvfu3SEgIAB27NgBnTt35s86dOgAR48etYibS5cuAYCJB16+fMn9p5IlS3I/QVEUbl+T7CB8pqencz+I4MKFC1C1alVdff327VsAkGVr69atNf4hwfPnz2HWrFlw6NAhcHV1leyXM2fOaMon/Uf0STaTg4MDf6a2Q2xsbCAkJISX0bBhQ2lsqa00ViR3CRdUvhqfItjb20OPHj007SVZLoJop3h5eUm0S3JezVfEA6Is69+/PwwcOJC/YzQa4cKFC5r6yMZkjHE9LupTxhj4+PhA1apVIS4uDgBMNEF4KFCgAFStWlWylem7Jk2agJ+fn9SG169fw8SJE+H48eMSnYk0IeLWHBAtAYDG3qVnVlZW3CcBkO1dLy8vsLe3520R7SnCiQjUN/FZenq6JmZAvICI8Pvvv0PPnj0hMDAQ6tWrBxkZGVKZoi1Fffb395dkjZ2dHaSnp/PfAQEBGl+A5FpgYCB/78mTJxAfHw+ICBMmTICMjAwuzwBMOlGt++rWrSu17+7du/D27VsYN24cMMbM+uFqneri4sK/B8jVwzROAAA+Pj4AYOJxtZ6h/ltZWWlw9vLlS90YhYgz+p/85xIlSsAnn3wCAPo2PYCJjmvUqMHlBNl4jDGu4wBy5XhERARs2bIFqlSpAgCm8Rblk+jbE0+/evUK7O3t4dNPP4U5c+YAgFbHPnr0CGbMmAFlypQBRVEAEcHa2homT54M06ZNA8YY2NnZwVdffSXR9sOHD2H37t0wdepUuH37Nri7uwNjDLy9veHRo0e8jZmZmVCrVi1ARFi8eDEAaH1xAICTJ0/q4gkg177p0KEDAIBk1xLO6HJ1dZV4W7TDxfvt27fX6H61X0LttLe3BwCAn3/+WarX3d2d2xM9e/bUyBg1PHjwAABM9Cnac0FBQRJfE4hjjIhQtGhRTg+MMShQoICGN0UICwvjdhbBDz/8oBvLJFkfGRnJxwnAZB+q/XgR32QPGwwGGDBgAGzZsoW/Z84XEMHPzw+Cg4N1/Xl/f38IDg6WLrFsNR2ox69nz57Sc7J7GWOS78gYg06dOnF7kOwyond6PnToUM5/BIgIZ8+ehRkzZsCqVaugbNmyXP4YDAaNneXn58flkoODg0bWvXz5kpdNesXNzY235c2bN/x5t27dNP3bu3evBo8AubqKvrexsZFs2cjISF1dTHEr4j2DwSDxUWpqqlkbumDBglzGGwwGSElJ4XTj4uLC9QfZUGTXuLq6Qnh4OHh7e2vKLF++vNn4DcH58+cBwGSnEO0WLVoUAHJ1l4ODgyRL3dzcuM59/vw5L0vPblfbdtQWJycnLg8aNGgAX3zxhdSu/MQvwsLCzPrGTZs2NYtrgtKlS/P/7927B/v374eVK1dCqVKldHXRpUuXoEePHhAYGAj169fP04YnOUH6Rq1z1X0B0PcB8w3vPW33Af5RUM/yikA5b0uXLo1v376Vnp06dUpamSvOHIvfxsXFSTnaBwwYwL9xcnLCI0eOaFbl1q1bF3NycnD8+PGalRW0Qoq2DKekpEg5aGn7NwEdOOnh4cFn7amdUVFRqCgKJiYmYk5ODlarVg0VxZSWQHyGKJ/rpsaZ+IzaSwcDi8+OHz/O2zl06FA+w+3k5CTlRd+6dSueOHECGTOtTCV80nNK8fDVV18hYm4KAlqJQd8CyKslxdzudMAkgGm7rDgOjDGcMmUKPw+G3qPyxN9iv8VVOKVLl9bQE6UCcXNz04wFpexo27atBm/FihWT+tOxY0eJ1hARb9++La0qc3R0xKJFi0plqleHiWWuWrWK8wM9oxUvAwYM4Ks+RJpAzF2lQBedcSH2rVKlSqgoCn788ceavqnHTu/MNxrD5ORkZIxh4cKFpVWmvXv31uwao0O0S5cujX/99Rfvl4ODA08ToygKbtu2jdOLvb09duvWDRkzHVZL+b/FsSdaIh4Un6u3W+vJkePHj0uragBMZ9I8fPjQbB/E1WviOOzfvx8Rc1fJ0koRKrdo0aIanNIZDXPnzuXfOTo68lRylAdcUXIPi6a+L1y4kPdJ79BZdeo4ROTlHjt2TNNOkaePHj0qnVlEK1537dqFiIhnzpyR2kJ/T506xb8Td8v89ttvkixwc3OTeEy8T6soKZ+2WA8i8vMnKXUI8YOaJsaNG6eRC6Jeof7Tc1rFKF60KllcRc+YaQcVnXlG9VLqGmqniDPa3Ubnl4o4o1QIlStXRkTL8pMxxnfGiTwt0r27u7tGj5EMUn9njq5pFx71YdWqVbr0QnwkrlRmjGnkp7hyXNzVrCiKdLagKCfEsQ0KCuLf2NjYaPSDSCeMMV2ZjGiSy2LKJUr1Q7hRFNPuAHF1GPVBBDVdivWp5Rn1ISYmhu9SNbcbjmhUtCnEMxDUuxYzMzP5DhM670atI+maNWuW7jlWlStXRqPRKPGgSINq+yYrKwu///57s4eTE7/ojRGNj6izEJHLDPWKf0XJ3aEfHh7OUwd/+eWXyBjjqZsVReErw+ld0knly5dHo9HIaZBom+i3ZMmS/NwN8bwye3t7vH37toSzWrVqoaIoWKNGDTQajZINQ6ty9+7dK90XzwKiHVdk24m2ooODg4QzPdtOtNEsjYObm5uU21+0iem3no1NdEEr2NX9oG8XLlwotYV4gtpKK8qdnZ1x1apVEn0fOHBAs2q3QoUKvA20ctXGxkaSB+rz5+j+27dvJZ1KOo70MWKuzhV5TCxPPOvE3d0dL1++bHbn27lz56S+066rmJgYSV6TrSziRfyO0jQBAJ45c0Zja4njtmrVKo4XeofsN1G+iPX179+f27hi3+kAeLLfEhISsFevXmbtaJFmnJyccNq0abr0mZWVhWXLluVto3McT548ienp6dKuTfp2+PDhEl2Ldjhdqamp/LwMcQwQkbdF3D30yy+/aOh6woQJFnmB9Ira7iNbpEKFCpr+qW0iOku5TJkyuHHjRl7umjVrpLGlXbZWVlZ49epViV6Ilpo3b66pTy0LateujYoip09njOGJEyekPjRp0oQ/EzMaODk5cTn/zTffSDRep04dSUYSn23btk3CmdpXUxR555uab0n3k36nnSKOjo4avanmW8ZMvnh2djY2b96ct8HT0xMXLFiAiIjPnj3j9YsX0RmBeucbYwx/+ukn/lw9tkRnhQoVkvhWtHcbN24s1SHuSBXrofET/Rx6rhfboPJDQkI0fqqYpk1tS9EK9l9++QWPHj3K6yhSpAhPAUftUdt2ZGNPmTKFl29nZ8fT7SmK6bzniRMn8nLv3r2roRfxjFlFUbBMmTJcppOtpaZrkV6o7g4dOmhiIojI7SjGGM/G4erqyuMshJf+/fvz8VPjbMuWLboxCtpBApCbVlPURyLOTp06JaXVE/tMsoviF4piOheO/qczmsi/F8+qEsvbvHkz/4biQSJdk08mpqElfaa2v2xtbTEjI0Oia8TclG1qe1JRFO6LJyUlSemWo6KicNGiRdz+6dOnj8YXBzDtqn78+LFE2+LYnz9/nsujPn36WIzPmGsn9Q8AsE6dOmg0GiXZK/olol1btmxZjhcR50lJSVizZk1UlNxzjXJycqR0xSIfURrBxMRESQ6qgdozY8YMKU2veJEtKdKZJXtQ/FbtA5LsFcsU9TB99+uvv0rfiLabOj6qKEq+fAF1W/KyQen57NmzNTaR2q/U2wFF/aLz7UWeJhBtJjVO1TpH/Vf0A6pUqYJTp06V7CxFUbj+EM/I3rlzJyIi3+HOGOP6PSkpCePj47kvSUByycHBgbfBYDDo2tB6dgiiHH8SZas6biXaIWLGFL24MpUZGxsrxa0QUbKZ1M9oBzIdCSOWSVkPKDOJOsYt9pXqqFOnDm/PrFmzNGO7Z88e3tZZs2ZJ9gbtMHV3d9f4v6Ghoagophib2v8V+UjUEYwxTfyC+m9tbS1lW5o4caLERyR3ypYtq5FZIn2KbalUqRKPWbm7u+OhQ4ewd+/evA71+ZmEw7z4T7Tb9XSuCGof8H3gw+TbvwTUglAEteEnntOGiFx4tWzZEj/55BO0t7fnQpu+FZUlCcOyZcvy+7a2tlLeZwrKlC9fXnMeAQBgZGQkF7AGgwGPHj3KD2AHAGzUqBHeuXOHt5Fytjo4OOCjR49w1qxZXAB/8cUXyJgpMNeoUSPe19WrV/Nnrq6uiKg/+bZnzx68ceMGTpo0ifeRlHLXrl013yEi335rMBiwQYMGCGDK80xbpwFMkxF0CGxUVBQimtJGiHiwsbHB8+fPS+NATv7WrVs1SvDYsWM8FZhohFN5zs7OkpNPcO7cOezTpw8/9L5OnTr8m8DAQB6MUwsZMjZFoJQYtra2+Ndff0lj0bdvX2TMlM5mzpw5Et4oDz6dlaFOwYCI+PjxY00KBRKGVOZ3332noafw8HCeL5sUBY3ld999h7///js31iMjIyWaEOmcgsmurq4SzhQl91wARVGwS5cuOG/ePP5bPXbmJt8aNWrE2zV69GhcsGCB1E/12Im4pmcAwPMIN2zYEBkzOaLdu3fnSvHFixecB2vXri0dKgwA2K5dO9y5cyc/s0Ic8x49eujKGFGOUNpJPz8/KQVlVFQUN/DVfVAbeXQO0OrVqxER8ccff0TGTBMGiLnGj42NDWZkZEg4JT4vVKgQbtu2DRljGBoaiiVKlEBFUdDLy4unUKlatapED1FRUfjy5UtERD6hSgcB6xnyiMhT4n3//feadu7YsYO/TwEB4k0y2OjsjEOHDnH+ICMzLi5OOjukR48evLzvv/9ekgVRUVESjxFuo6KipLzWJUuWlIwIRMS6detyx0jkB7WRnZSUJMl7tV6hc6PIqXF1dcVt27ZJuqN58+bImCm4N3LkSElmjR07VsKx6Fwj5gZNFi5cyI1FUa7HxcXxgKSiKPxMnbzk59mzZzXtpDLJ8RblJ7UvICAA169fr9GNYmogerdgwYKImBugLVOmDJfRIr3Qd+J5UHryk85WEM9CoHNsRH0syolTp07xse3UqRMPIpI8F2WMmDZQbXhu3LgRd+3ahfv27cO5c+fyHPFiqjK6yKkTg3PUBxGo3dHR0Whtba3RASTPKMUaY6bzYUV5lpCQgC1btpTSmAAA1q1bF4sXL85tihEjRvBnFJQUgehs5syZOGPGDE6jhG+xH3369OHtFHFfu3ZtnD59OudBosF27dpxnib7RoQnT57grFmzMDY2VoNLe3t7Pkbqi3grLS0NX79+zfEFANi0aVMpnRRNWAYEBOBff/2Fy5YtQ0UxTQwMGTKEfyvSb/Xq1XkaJuofpRQKCQmR6Fek26NHj0rn+trZ2XG8UDCC/q9SpQrGxsby3zSunTt31rVtFEXBNWvWYK1atXi7ZsyYIeFMbfeo5Ra1mfpgbhxEPcUYkxZ7UV1kL4oX2Xzdu3fXtdEowNK7d29NW8S2/vDDD8gY4+dMiin1SDeI7XNwcMCcnBxERJ46zd3dncsDGxsb6dwkWohCvsDgwYP5b9Kd06ZN4/hRT759++23/IwtxhguXbpUCrz5+fnh6tWrpXEAMJ2Fd+jQIX6eMJ1fRjIpJydH0nFqvIg469u3L6+vd+/ekl0wZ84ciW9DQ0M1tp2iKPjZZ5/x4G6nTp009SEinj9/Hvv27cu/q1ixInbp0kWqCxF5KkZHR0csVKiQxtYRLzV9Llu2jC8SYIzh8uXLMTExkQfBRJkUHx8vyelNmzbxvokTxr6+vsiYKZB5/fp1ydaPi4vDY8eO8bNVKlSowJ/NmzdPIyPF/rm4uGCFChW4DixfvrzE75QmioJ8ZJfSIhkKdIk20cSJE/mzCRMmIGLuxIC7u7uE79u3b3P95OzszGk5KioKw8PDOQ5pkQjVR99v2bIFP/nkE/7ezJkzMSgoiPfB2toaZ8yYwd+/fPkyl41hYWGcPkNCQricFxdKMcZ4WkG1zy2mxTMns0T5pubbevXqcf2ekZGBu3fvRsYYtzdEP2Dy5Mk8LRdjTPIDRJ1IZRcrVkwKdtKiDZHOCNSTbwCApUqV4otlxbF98eIFp7PmzZvjwIEDeb3EJyQfCa5cucKDWADymYRxcXGYlpbG6UxMXasX2xBtqcmTJ0t+KsUuxMA32VLR0dGoKKYUkWKK0k6dOkk67syZMxpfgGxsOqMYINdXa9OmjUbfAgBOmzaNp8ZVFAULFy6MHTt25DRpztai95csWYJz5syR9DYFZO3t7TExMZG/u3r1ajx79qzUhuLFiyOAadFh586d+btFixblixybN2/O/XcKXJYsWRI3bNgg6Ye0tDTpvLuWLVsiouzHbt++XcLZ5MmTeZ1BQUFc34qytGnTpujq6sptUOI9RTGlBxVtG1GmkVyj8lu3bo2MmXyWs2fPIiLikSNHuK4Xv0tMTMTt27djs2bNuH+gKAr+9NNPEl0TiDzYtGlTLiNE25VwLfKf+gx7eib+9vf3x2nTpuH58+f5mW+KYppwoIVwrq6ueOvWLYvxGXPtVOusqlWr4ogRI3g9xEsGg4GnPDUYDLh//36ONzFIPnfuXDx+/Dg/E3blypXc5yS5JdoMNKbz5s2TeI78dAJqz+7du/nkm5ubm2RHkw8o8iZ9R7E20Z4gm5kWiqrlsuhXiv6YuJAhLi4ODx8+zL9BRM1iRb24HKJlX8DBwUHyBSzZoJRm/ssvv9TEn9R+pdh/cQEVAPBFiYRLsofEmIHo++pNMomyjmiN2kb3ExIScNWqVTht2jSsXr06Ojo6cv6m/ooLOMn+oSM2SJ6NGzeOlzlp0iRElOMlgwcPlvoj2tCW7BDyAa2trSVdrI5biXURLhmzPPlmb2/P6ZPODxRj3OpnWVlZfPNCaGiodI6hmNq2YcOGuHDhQo39TRNPop1C9NKjRw/N2CIipwM7OzuujwoWLCid4ZmUlCT5v59//jnnOWov+b/ER2PHjsWPPvpIGgNRZmVkZEi8u3nzZgl3FEOlWBD1iewbtY29cOFCjS++efNmHr8i3lTzHoBpnoJ81bz4j+z2hQsXauYh1GDOH30X+DD59i8BtSBUgygMFcUUbE5NTcVp06bxg2tjYmJw165duGzZMmzevDmmpqZKgXUicvp2zJgxkgEsXiSsxHtioFG8evfujYiyYU8B0Tp16uC4ceMkYb99+3b8448/8Ndff8U5c+Zgw4YNpfYxxrBevXqIiJIzdf78ed3JN7rU7QUAvgpCrcwyMjK4AKHvnJycsE2bNlIOcXpmbW0t5UzXw2fFihWRMdPh6fv27ePnOdF7hQoVktqqNsLpPCixb2owGo38bBWxvzY2NtimTRvs0aOHRvkeO3ZMKkPEhfjs8uXLeP36dakdJOBFXCiKogngzpw5E9u1a6fZ2UCHQYvtEVc8ivf1aIsx04HLrq6uXPjSOUqiMTZmzBguDNXPCGfZ2dnS6kPxTB1akZeQkICIyCfVSNHptU2P7oieaOxEXFMQCMC0KwER8enTpzynOT1zdnbGESNG4NChQ6VJBr026PFmiRIlzMoZkiNUnq2tLVeGdIkG1LRp06SgEV1t27bFxo0bo6LkrkKkYEqlSpV06Uz8vWbNGv4/tT06OpofukxyJTg4WINfRTGtAProo4/4OMyePVsKTIhXamoqn3zr1KkTP5MwOjoap02bhqmpqZoz19zc3DA1NRXLlCmDiqJgcnIy7tu3j6+woRVZlE9e5Onz58/zdtLO26FDhyJjpp2MIo9R31xcXKRVfBMnTsSSJUvy32PGjOEGOGO552GRw6EopjO+xo4dq4svUa+QTBXPnSBeefbsGc6fP18KnHt6emKvXr1w8ODBWK5cOS5f9GjyzJkz2Lx5c1QUhU/CM8b4uSmMMS5HGTOtgKPgc2pqqkQ/hDMaY7EP1E5RB5jjDcZMgYaffvoJnzx5gvPnz0dExGHDhmnejY+PR8Rcg1xRFB4YpHYhIrZo0UK3riFDhvCVdYqiSMEGvfcdHBxwzJgx2KtXL44fkhMGgwFPnTqF169f5+Xo9Q1Af0LNnF5U/3ZycsLt27cjolaHK4qCBQoUwDJlykgB4ilTpkg7chljuGvXLly7dq2UVx4AsGbNmhp5piimHZ7mzlEYMGCA1Jb+/ftrZFlKSgoqioK9evXi9y5cuIADBw7EK1euSIFZPRlN4yTKXbXuZIzhgAEDzMpTRJPeJOdGb3zookCYns3CmGnnhnggdoUKFfi34qpuJycnPHfuHK5YsUKjSwsXLsx3lanrcHZ2xtjYWM35UqINo3c4PIDpPGE9+69gwYK6eG7UqBFfsa5XnsgPtJBBtHvUcov0O+lNc+OwevVqzbkGiqJoJsHUFz0T6VO00UTZpG4L1TF69Gj8+OOPEcC0iGXp0qXSbhE92lAUBWfNmoX79u3Db775BgFMGRVEOiAaoLaoAyP0Hq1ij4+Px3379uGuXbuwc+fOedavHhcKnuZlY4u0e/r0aUnHiXj56quv+I6BwMBA/Prrr6VvRRuT2qMoCg8609gFBgZKEwv0/dixY/nuB6KZjIwMfPz4MZ45c0ZaDEO4FPXOzZs3+a4QRTGda1O2bFn08PCQ7JH8XKJtJcoQGrvhw4dL7xNPibqfbC5FMa26FVcZq/EurvgVz7wRQd2/qKgoTEpKwoULF5o9a1YdZGaMYcuWLfH58+d8goJsH8ZMgZvXr19r6hPbvXz5cr4IQrycnJw0u1UBTIshe/bsaRbXlvQ+Ywx/+eUXzo8kZwBMk5tqOgIw2VJ0xp7IZ3npT3P3Rb5V28I0YUH2hugHkK9AZZjzA9S2MYDJZtabYIuKisIePXpIC27Fbx0dHbFJkybc709ISOAZJBhj2K5dO65vxXITExNx7969uGnTJuzWrRu6uLhIOKtevbpUj42NDd91QRPToiwSYxtqW8rV1RUjIiLM0qy9vT2OGTOGl6s+o+jUqVOSXZeUlITdu3eXbE71eeQAuTEEo9GIffr00ZybnJf9qX43Pj4ev/jii3eSKwCmybXk5GTJP6OJeoDc3Rjqc/cAQDrrkrHczDDibklFUaQJBwDgu0NE+qVFQYSzly9famwbtbw6ceIEz5JA92khMU3CkS64c+eO9B7RlKIofKKNbOvk5GSsX7++Ri4WK1ZMkp/ixArVqyimCX0CNQ+6u7tjnTp1JNtV3f78jLuoT9W0ItqiNEluKT5jqZ16Mo0uvbOL3d3dpclx8fLw8MB+/fphr169pPZTm9U7xkmuvnr1SvKryO8iXatnc+j5cJUqVZLiCfROYGAghoaGcpvA1taWy/Xw8HC+IEe0XUTbbebMmciYyR80Go1SG8RJPtHmU9M1PdeDfv365Us/WLJBAwMDNfFKdVyOxo12P1qSN7Nnz8Z+/fpJMTRa4C7a2GKdRqMR+/XrJ53tl5e+VfN8+/bt8eTJk7h582YcN26cZqc6xXhfv36NERERvJ6iRYtizZo1EQCwSpUqiIg81qN30YQw4Y4mBM3Z0Oq4lWi3qp+p8TJ37lz+P2VnouwVlp4hmnboUvYecZHs+vXrNYtI8pIp4jv+/v6asVXLCXrf3d2dbzZRj5uzszOPdYgX+d7EDx4eHtJ3iqJoZJY6Fvj999/r0g5lSTMXqxDlA2OyL75nzx5dfl+9erW0oNBgMGBCQoI0Sa3Hf2J/6DfRqBry44/mBR8m3/4loBaEarAkDNVCT0+oiwRs7tu8Lj3mIeNp27ZtktFPzJmfNqrb8Omnn+KbN28QMfcQTUUxrXDQ25IrXur2UToJtTJDNKWuUqcdsYQTsQ4HBwcp8G0uMKH3vZWVFfbv35+vNqQyVqxYwbf25qXkxe+aNGnC2xAcHIytW7eWxowmAAj0JgdEWLZsme6h7SRs9AwovYt2YB08eFDaiZAf5W3ueY8ePTQ0gYhYoUIFVBQ5TQ09E+HRo0e6h86r/1cbSubaY4lWFi5cKK3yFp916dKFt+n58+eSM6bHw3nxqbq9jx49sihH1HIhvzKAxtVoNHIFRKl6yOEZNGiQLp2pjTsxfaEe3k+ePIkTJkyQ7uml1rGysuIpt/TwZMlgNIdfvfuWjAORp41GI5dVtJKXUv727dtX4jFz9Ddv3jw8f/48n6gyV7d4gLSHh4f0niiDRb1So0YNVBSF7+4yxys0ca5HiyEhIViuXLl86w89hyshIUGiUzrkXlEUvHjxIscZfW8uVUdevCiOp4ODAwYFBXFjlZ6TXm3fvj0vu379+tI7Hh4eWKlSJYyJidGd7LJEI3rv2dnZmR1bknVqmaxHey1btsRLly7x3Yp51c0Y0/AR0YO4+13UAWq6onQ5YiAsL15S05A52cMY422he5GRkZqxpx1d5gzgW7du6ToTav43J39sbW35Ksq8gGSdpXpOnTolpZQV8Vm+fHnMycmRgn/vK7cs6TXxuZWVlUUb5n0vsTxzz8V6fHx8NHaPWm6Rfhf1pjl4+vSpZAOp8ZCf9qttNFE2lSxZUmqLXn/VOM0PHZobN0VRpLaofYH8jHV+xoFWuavHIT/ydd68eRodZwkvltpNfd6yZQu6ublZtKvzwr0eDtR6h2RF5cqVJbn0T/CDmo4QUVrprHe9efMGt27dqvHh1JednR22adNG+m3O7vu7/XNwcOArq9XjERUVhVeuXDFbn0jH71JnfmQIXeoFBfR9fuuitNgGgwGTkpKwf//+2KtXrzz9lrzaT2OvtoUppZtob5AfoOY1c7SsZ1MFBwfzSUNzeKP/e/XqZdaOMTcWejJGT6/TxCwtGBP9VPpeXFySn9jG37k6d+7M8Ux2ndiWoKAgs3ZdQEAAfvHFF9igQQP09vbWlUH/VDvNXWqeJXpVLxS25AMxJi+4y6tOcTJfpF8/Pz/Jz0NEs7YNgEmvPHr0CF+8eCGl4FX3z8rKiu8yEMsR9REiSrtpzdlAM2fO1KVL8aJFIiKoeTAv21Wv7rzGTv1N8eLF8eDBg7q41ovPmGvnf5oG1f3RozGSP2q5QX6XufL0ZF5ERASns/zYIQaDAffs2aOxJUXbrXr16qgopjR6Ip2JPEJ0Jn7n4uKiea4HP//8M3+HdvXk1We9PlFsRaxLjMvplUk+pbnxonrIr1Tb2Hox0kuXLmFqaiqfsH4XGtPjG3pWt25dHuNFNE0sU0YrtS3n7OyMvr6+ZieKzdVpzoZWx61Eu1X9TI0XSqeoKKZFIopimtxFRIvPCM6cOcP7qZbNf5cnxbFVywn1e+JvS3zNGOO8q170QoscFUXRyCx1LBDRlBlLnaJVXb85PNjb2+v64ocOHdKNmd+7d0+zsPddcMwYk+Yh1PAu/qg5+DD59i8BtSA0B/kRhqJADwgIwE6dOuGlS5d0v82PQNV7LyUlBdevX89zXCuKwldXKYopL2ydOnU0KX/MlakophVJgwcPlvqbkZGBLVu2xJYtW+K6devw1atXeP36dbx+/TouWbJEuqZNm4b9+vXDfv364ejRo7F8+fKYkJCAz549w7/++gv3798vnYeBiDwHNABw49EcDmxsbLBKlSo4ffp0fPXqlQafeTG0t7c3HwtzY//bb7/xdlhS8urvDhw4IKUeoTaLEwAEepMDajh48CDWq1cPPTw8+Dkd8+fPx5ycHFy9erVFXFlZWeGQIUOk8jIzM7FNmza6RtuiRYv4GPbv31/XkXZwcMARI0bo0oSo9JYtWyY904OcnBxcuHAhV4J50Sc99/f3xzZt2kjtpWvQoEEaw8ecsa0ouWfbETx69IgrJtrlYMlAs7W15SsC9cbAXOpJRNOuITJk1JMQli4nJyeeXxoR8cCBAxgQEICFCxfG27dvY+fOnbFz586YlpaWLzp7+vSplKKC2kF9JcOFJlTzO1aFChXS5LDPS66RnPzxxx/fiacdHBwwNTVVw9Mi/PTTT5w+adcqIuL06dMl4yIuLg4bNGiAQ4YMwR9//BERTca8OVlvzlhycXHBJUuW4PXr1zV65c6dO1i8eHEsVqwYbtu2zSKvZGRkYPv27TXnvlWsWBEvXbrE+UjcVWIJz/S7UKFCuGjRIr4akuD169d8Ra6YaseSbhw6dKh0dezYUVo1/s0330irv/ToiIIJiqJI5yW+fPlSWj2n54D7+PhgcnKyRqdawgWAyfkQx1Z8x87Ojss6EQ4ePKgJaJJMJvj55/+Pve+Or/l6H3/O+97snZAhWxIZghDECGLU3lujJKVVRWsWtWdVVW1VarRae+9Se9cosSpK7IqERJDIen5/3N85Pe95b65o9fPN83q9X+Se/Zxnn3VY9A4Q/SwtLbF169a4fft2vHnzJuPNJUuW4IcffihbCBQEQVEHSOdCSp/0s7Ozw969e2OXLl3YjmFjMq179+44ePBgUV943PO8g2g45da9e3dMSEiQ0RKF1NRUxetglGQDL38AAFu3bq1YpxLwsm7UqFGyhXWqyzMyMnDGjBnYvn17bNmyJQ4ZMgSvXr0qquuvv/7Cjh07KjraSvhr0KABfvnll1i/fn3FU+XS/P7+/jh48GC8c+eOqk1oij4wxu+0Hr1ejxUrVsThw4eLdCbNM2XKFDx69KjI7pHaRPRvqd7UgkOHDinu+Nb69Ho99urVSybPedlE6ZF/n1aKNynOXV1dce3atYrzo6ajXFxcsEGDBrh+/XrF8V2/fh179+4tOv2gNQ/Ozs7YoEEDnDZtmmgeeFt5w4YNousd+Xx0Rzshhrclly5diu3atcO2bduKrovhb2bQwguAIWBGd+BXqFABFy9ejP7+/hgQEIDJycl44MABkT1Ly1laWmLp0qVVaVZJxgQEBODnn3+uKisQEX/88Ue2qeR1+IHiWvpWJYWpU6cqnuAh5O93cmbNmqUabKMn76UyUumEsLHxmcPTdA7GjBkju1qMhyVLlmDlypWLjE+lvP7+/vjxxx/LbG/6zZo1Sxa4M6WtWrVq4bNnz0TvaPIfrUt6rZ3aZ21tjXXr1hXxLa8fNm7cyE4nKb3PfPToUZP0Ji/L161bJ/L/6L9Tp04VnSyV0tn169dlV1drzYW3tze2bdtW5DtJ5Rx/YoRer0ll+a5du9iJPWnArKi6iF6Zr5WPEHlAMisrC5s2bSrS99KALb8IJ00nxLBQVL16dVy/fj1u374dv/32W/zqq69w0qRJ+PHHH4s255orQywsLNDHx0f0frkgCBgfH49Hjx7F7t27Y9OmTTV5hv7u5uaGo0aNwszMTPY2klbbnTt3Fj1XQel31qxZ2KlTJ5GfR0Fq2/Tt25fROZVLvG0j5UE+NkPHOmnSJHa6h4+HTJ06VfSGMgCIrsK+fPmySH5Kv4YNG+LTp09V5VZRebBp06aaN4Lw+XmZMm/ePNHGDKmsUIvPaPWzqHSm1+uxWbNmuGnTJuzVq5fqLRtqNDZkyBDcu3evLP4kzS/tm5+fH86YMUMkw7/77jt2nVxISAijM+qnJiQkiE7u1K1bl20iJYTgpEmTZDE03najfEyvnKa/8U9rUDrjyzVp0sSkuNzz588xLi4O4+LicPHixbhkyRIZrRNCFPUXP/amTZuy36Xzzb+FHRgYiN999x0uW7YMly9fzjbwK82bnZ2dyK/k7SxT4ObNmyJZl5iYiGFhYZpvbvNzb2lpyfiRv+6VQlZWFo4ZM0Z0C0BR7AalvEo2NB+34t9b3bt3ryhNyU7Mz8/HCRMm4CeffMJoiV5VrZXGQ05ODo4ZM4a932zK2Ly8vLBTp06YmJioqM9tbGwUYwaIhqugKQ2qvU/I48/Pz4+dTlfrn7OzM16+fJnF4nmZtXPnTlkskML58+c140VK7fXs2RPv3LmjSpdaMfNvvvnGpEVbXhZWrFhR8ZkLvj3aVlH8USkQREQogX8dVqxYAQAA7dq1A0dHR5PK3Lp1C65cuQL37t2D69evw7Nnz8DKygoqVKgAPj4+EBERAYGBgUbLZmVlQU5ODrx48QLu378P+fn5EB0dDTqdDl68eAHW1tbg4OAAvr6+4OTkBN7e3uDt7Q16vR7y8vJgypQpMG3aNMjNzQUAAEIIFBQUAADA8+fP4bfffpO1Reuk/axWrRrY29sXAyaLBrdv34aUlBQAAIiOjoacnBy4cuUKPHnyBP7880948eIF2NraQtOmTSEsLAz0er1iPRSfp06dgvT0dEhLSwNXV1ewtbUFNzc3KFu2LLRp0wZsbGxkZQMCAoAQAnv37oXg4GC4efMmHDlyBAAAevbsqdl3AGBzkZubC5MnT4bp06dDbm6uaB6KG7Kzs2Hnzp1w6tQpuHDhArx8+RI8PT2hcePG0LlzZ3ByclIs98cff0CfPn3g8OHDAACKfczNzYUZM2bA7du3ITAwEMqVKwcNGzZUrVM6h0Who+TkZDh69ChcuXIFLl26BJmZmZCXlwceHh4i+oyNjYWQkBDNuq5fvw4fffQRHDx4UJYWGhoKn376KYSHhyv2Mz8/HzIzMwEAwM7ODi5dusToUMqD5cuXBz8/PwCQ4xMAYNKkSWBnZwcDBw5U7OfBgweZvBkxYgRYWloy/rxy5Qrs3r0b7t+/DwUFBeDt7Q3ly5eHd999Fzp16gSCIJiMW1Pg119/hbVr18Ldu3chNjYWKlasCAAAmZmZYGFhAZ07dwZEhAULFsD3338PV69eBb1eDx4eHuDs7AyEEMjMzAR7e3sIDw+H1q1bQ6dOneC3336D3bt3AwBAjx494MyZM3D8+HF4+fIl+Pj4gI2NjWh+leSkVEaeOHECDhw4AIIgwNSpU+Gdd95Rla88JCcnw4MHDwAAIDY2FnQ6nebvUsjNzYXdu3ezsXt7e8ONGzdgzZo1cPnyZaAq3NPTE9q2bQvDhw+HgIAAAACYMGECAAD069cPSpUqZeq0iCA1NRVOnToFubm5EBoaCpGRkbI83333HSxatAhSUlKgoKAArKyswNXVFcqWLQuOjo7g4+MDT58+hZCQEOjbt6+qjktKSoJnz57B/fv3wcPDAwAAbt68CYIgmKwbleTnmTNnYMWKFXDmzBlIT08X0Ut4eDicP38eAABatGghw9P69etVy/JjUdKpUj33/PlzePbsGQiCADVr1pTNbWBgoKasUxsfD+bUaYpcBgA4dOgQAABUr16d6TJj7eXl5cGFCxdU5dnevXvBycmJ0SjfF0rber0eYmJi4OjRo6pjUIPMzExo3rw5nDhxAgAAtmzZAsnJyYCIcP/+fcjJyQFbW1uIi4uDihUrQmZmpmiOzIFr167B8OHDYefOnVBQUGCWLs7IyIA//vgD9Ho9WFlZwZ49exRpMCYmhpWhuF6wYAE8evQIAAAaNGigqDukIKXf8+fPw/Xr16GgoABat24NFhYWYGVlxejawsICXr58Cbm5uXDnzh3IyMiA3Nxc8Pf3N8m2k9ovPF1TaNeuHTx9+tRs/c7bRK9evQJCCCxcuFCVP7XkeVJSEly/fh0ePXoE5cuXZ32h47hw4QIkJycDAECrVq3AyspKVN7b2xvu3LkDR44cgcePH0N4eLgI1z///DMAAIwbNw569uxpkm6hQOduz549kJaWBgUFBVCtWjWTx8bDqVOnmO4cN24c+13LztLSZa9jn23fvh369esHOTk50K5dOwgJCRHJF95+W7BgASAiREVFgaura5HtNx7u3r0Lp06dUpRZNjY2YGlpCba2tvDq1asi0xGFP//8E/bv3w9btmyBvXv3Ql5enkxOJCUlwbJly2Dfvn0gCALUqFEDOnToAPXq1RPZ+gDG7T5j47t37x5cu3YN8vLyIDg4GLKzs+HFixeK44uNjYW4uDiwsLAwC58PHjyA/Px8sLGxAQ8PD8jJyYHz589DRkYGFBYWQrly5UQyq27dusym0YItW7bApk2bIDU1FRo0aMDae/ToETx8+BBevnwJ9vb2EBoaCu3bt4eIiAgm5x89egT9+/eHTZs2QWFhIQAY9GCNGjVg6tSpUK9ePZmMfPDgATx48ACsra2hU6dOJs39pUuX4OzZswCgbG9QMKY3pbJczf/Lz8+HixcvwsKFC+GHH35QpbNZs2bB77//Dnl5eeDo6AjBwcEQGRmpStdnz56V6aPY2Fi4d++eSBbcu3cPAIrmp5piS9G+UNvut99+g7t37wIAgLu7O8TFxcHYsWPBxcVFEb9adp2npyfMmzcPbty4wfwyAAAvLy9o3bq1yMbWAqVxEEIgPT0dsrOzAQBAp9NpxkTy8/Ph6tWrkJubCyEhISIbOCMjAzIzM+HatWuwa9cuZi8EBgZCq1atoEaNGpCbmwtubm6ifp05cwZmz54Np0+fhtTUVMjPzwc7OzuoV68ejB8/nvmprwtLliyBR48eyeRSRkYGnDx5Eo4dOwalS5eG/v37q/qWW7Zsgc2bNwMAwLJly9jv+fn5MHnyZObjnD9/ns01pTUqP3nbtFWrVvDOO++Y1P+i8GB6ejoMGjQIVq5cCQAAiAjHjh0DS0tLePXqlWh+Hz9+LOrn64JSP3k6I4QAIcSk2FtGRgZs3rwZ1q1bB9euXYOcnBwoLCwEV1dXcHJyYuMuV64cxMTEgK+vrypfnzp1SpXH1OIJ1AdUs7+V7ImxY8fCw4cPoVSpUvDFF1/IfKSkpCQ4ceIEnD59Gnx9feH9998HX19fkQ26cOFC2LhxIwAAHDhwQNYXDw8Pk+JyUiiKL2Bs7Grj5+HmzZtw4MABOHbsGLi7u4OrqysEBwcb9QHNhaLEeG/dumV0fFevXoWdO3dCUlISPH/+HO7fvy+q09LSEnQ6HTg7O0O9evXA1dUVXr58aZYdZmoMRgq//vor80UHDx4MDg4OJqXxUFhYCMePH4eTJ09qyhep7VNU/563Nxo3bgz3799n7d25c0fm/1J5phQz8fLygpiYGGjYsCHUrl3bJFxJgfJmfn4+VKpUCfbs2QOPHj2CV69eiew+aVzAlDoB5LyZm5sLY8eOhZkzZ0JeXh4AAPMpzFmHeB1/hoeSxbcSKBb4448/4JtvvoFXr14BgNhIKoESkMLJkycZrdSrV+9f7k3xQlpaGpw9e1ZkbBYl8GMO/C/jswTUIScnB548eQL29vYmb9oogRJQg7dJjhRnX3Jzc+Hhw4cAAODv7//afSuBEiiBEiiB/z3gNz0EBATIFi5K4P8WlNjYJVAUePjwIdsQUWJrlkAJlEAJlIAUShbf/scgPz8fnj59CgAALi4uxbKr5k3U+U+D1hheZ3zmlv2n5+lNjP+/Uuc/Df8LY/+n6fp/YW5LZG/R4b9CL/80T79NePmv1Glue29T2puAt2n+/iv88L8gd/8XcPZfmYf/BZ5+E1CCl6LD26Rz3qY63yad+l+C/zpuduzYAbt374bbt29DQUEBlClTBuLi4qBz584mn7xVgv+Knvuv6PH/Bd/4bZJNb1Nf3gS8TX35r8B/hTffRF/eJnlWLGD2hZUl8NbA5cuXccCAARgeHo46nY7dN6vT6TAsLAz79++PSUlJ/3qd/zRojSEwMBArVqyIZcuWLfL4zMXNPz1PWmM0d/xvAqdvap7+SXgTNPFPj/2fpuv/ioz5p+fov4IXc+G/Qi//NE+/Cfn5NtHg2zR/5s7Dm0h7m2j3TdT5X+GH/wW5+7+gw/8r8/A2ybMSvLz9eNGCf1pX/Vd8vLdJp/6X4H+BJ/766y+sUaMG67v0K1u2LF68eLFIdf5X9Nx/xfb5X/CN3ybZ9Db15U3A/4Jc+qfhv8Kb5o7h/6oPWHLy7T8MhYWFMGzYMJgzZw4UFhaC2lQSQkAQBOjfvz98/fXXmu82mVNn37594cSJE3D16lW4e/cuPH/+HLKzs8HGxgasra1Bp9OBhYUF2NnZgYWFBUuzt7cHX19fCA8Ph6pVq4KdnZ2ojfT0dAAAk679yMnJgX379sFvv/0GqampcPLkSbh48SIAgGgMWuNRG9+YMWPgl19+gVu3bsG+ffvg0KFDgIgm4xsAFHEqLU/v5X7nnXdgypQpEBUVpXoHsdY8afXLnPFrjcFYe0p19u3bFxo2bMjuqT969KgMn0URS4QQqFq1KiQkJED58uUZLfE08fjxY/ZeVp06daBWrVqs/Pbt2+GTTz4BQgj8+eef7PecnBw4c+YMXLlyBe7cuQNZWVmQnZ0NDg4O4OLiwtqaNWtWkXnwTeDz+vXr7A7oBg0asCtSkpKS4Nq1aywtKipKVI+pPE/brFatGlSuXBn0ej38/vvvcOLECZP5oX79+vDpp5/C06dP4cWLF4rt0b8pP7Rv3x4mT54Mfn5+sjcTHz9+DKtXrxaNr0WLFuyaT7W5NQUKCwuhX79+sGTJErPniIKvry8EBARA2bJloVu3btC4cWNZnpSUFJg1axbMnz+ftcfjgm9DKs+zs7Ph3r17Itlrb2/P3reTQkFBAaSlpYG9vb1M7r4pMEe3mEIvauUGDhxY5Hnfvn07DBgwoMjtvQ5PF4dMLigoUKyH8pCW/k9OToYmTZoAIQSSk5NN0lW07ooVK0Lz5s2hWrVqUL16dfD29hblefz4Mfz888+wbNkySEpKMipb+H7u3LlTlXcPHDgAkyZNAkII/Prrr6K0mzdvQr9+/WDv3r0m6xVj8/Am0qR/E0KgQoUK7B2Q8PBw6Ny5M1SuXFmxDh7oHBYUFECLFi1g8eLF/4hNyP+fjqFu3bqQmJgI5cuXh5UrV8K8efPeOD9MmzYNdu/eDV9//TUcO3bMaBum8q1WuUePHsGdO3fA3t4egoODZW+8aUFKSgp7X7FHjx7w4sULyM3NVX2fyJieKywshD59+sCyZcveiG1KywYGBrJ3XuibPUVpj6ezvLw8kb6ytbWFzz//vNj8mYKCArhz5w48efIEwsLCwM7ODo4ePQrr16+HP//8E3Q6nSqP0fdvebsvOzsbFi1axN6iVaJrHneCIJjMY1pQFL1JCIEWLVpApUqVIC0tDbKzsyE4OBjq168vsnmLCqbQp5KcN2a/jBgxAr799ls4c+YMvHz5EhwcHKBs2bLg6+sr8g+Vrm8zx56YNm0a/PLLLyI+4m1lADlv0rZOnz4Nt2/fBnt7e6hQoQL4+fkVC98Wt83wNvh41G/evXs3LFy4EI4fPy6r55/SqTw/qPmAWkBtjcLCQli8eDEcOXIEdu/eDX/99RdYWlpC5cqV4d133zVJT1+4cAFat24Ner1eZtMUFBTApk2bYOnSpZCcnMze7bK0tARHR0cRP9SuXRvq168viw+YwxNdu3aFEydOQGZmJtja2kJeXh7zHywtLQHAcG231LfQ4k1z4P79+/DDDz/AnTt3IDg4GBYtWgQ3btzQLOPp6QkXL17UfBNIatMXxVYGEPMEfQuS5uf/LwgCvP/++1C+fHnR23s8vwOYz/PSv4vDhnkT5Yo7fmEMZ0rt0BiFhYWFiN8vXbrE4kGCIMDBgwdhz549/6rsTUhIgGvXrsFPP/0Eu3bt+tf1AG+nmGIvpaamwrVr1wAAoG7duux33idZvXo1LFq0CH766adise2MxbSUQE2HmwN8bPHBgwfw5MkTKCwsBEdHR3B2dhbJSXt7e7CwsAAbGxvIzc2VxRArVqyoGOc1N74PUHTeVKu3UqVK0KxZM/Dz81OM0+fl5cHu3btV7am3KU79JsqZa9MDQMnJt/8aXLx4ET/88EMMDQ1FvV6PAMC+4OBgfP/993HkyJE4cuRIfP/997FChQpICEFCCAqCgJ07d1ate/ny5awuWiYyMhLff/99bNmyJfu9QoUKonZp3fxHCBHlMZbfxsYGW7VqhdOmTcPGjRujra0tq8fS0hIrVaqEM2fOZGO3t7dHe3t7LFeuHNaqVQudnZ1ZXdL2rK2tsWXLljhy5EgMDw9X7JsgCOjl5YVxcXEinBFCUK/XK9YbGBhoEr67dOkiwkmpUqXQ2tpahA8lHNna2mL37t3x+PHjsrni66TzlJiYiP369cPg4GDFOsPCwlTHHxYWZvIYaHt16tTRHIO1tTW2aNECExMTWZ1a+T08PBg+1fqp9dH+2tjYYGRkJDo5OYlojM8XHh6OJ06cQETE1atXs7KIiGfOnMFOnTqhvb296q47NVqj/FIUmuD5TGvsPD5DQ0M18WBra4sDBw7EatWqyfrs6+uLP//8M6Oltm3bitpTGoOXl5esDWm7ZcuWNTr2GjVqyGRM+fLlsUGDBli+fHl0cnJSnXdCCPr5+WF8fDyuX78eV69erThHOp0OP/zwQ8zLy5PNbUFBAW7YsAGHDh2Kn3zyCc6fPx+fPHki4q1FixYptu/s7IwdO3bUnCPKRwkJCejk5KQ6R/b29jh58mTMzc1l7UrzhISEsP9L8cnjIyAggO3SkfKYIAgYEBDAcHb37l2Mj48X4S0wMBCHDh2KEydOxCZNmmBkZCRaWFhgqVKlsEuXLrhr1y5FufvBBx/guXPnTNJV9vb2In0lpfmi0Iup5fh5Nwbr16/HwMBAdHd3l81DRETEG+Pp15XJxvQspUEt/X/p0iWWLh1DUFAQenh4iGhOSwbXqFEDV61ahYiIa9euRXt7e8V8oaGhOHz4cM3xSXmXB620mjVrqtKZufNQ3Gl+fn5s7JGRkYp6jH6dOnXCx48fq9Lutm3bsFatWop49vPzM9sm3L17N9avXx/d3d1FNBEaGooVKlRAW1tbTdqT/v2680BttNjYWIyMjBTZaDY2Nor5o6Oji2yfeXl5YWBgIHp4eLC6pDZhqVKlRHNkZ2eHCQkJ+Ndff2nKmeXLl4twSe1pWk/p0qVx7Nix+PLlS1Zm7dq1IntRSc916dJFURdL7XZjY/f29kYXFxdR/4zNLQBg6dKlMTExURPXjRo1wlGjRmFcXBx6eHgYtaUCAwOxSpUq6O7ujpaWlmhpaSmb55CQEFywYAFmZmbihAkTcPXq1ZiTk4P379/HFi1aiPpsZ2en6HtQHrt48SIOGzYMy5Yta7K9p6SPlPKo8RilB51Op0ozUjlfvnx5bNy4Mfr5+aG/vz8SQtDKykpRhvC/BQcHM5sXETEwMBCbNWvGcKYEa9asMYk+1eQ87YutrS1GRUVh+fLlTbLrpfPk6+uLAwYMwB07dqj6v7a2tmhtbY1WVlaackLqp1pYWKCPjw9WrFgRO3fujBMnTkRCCJuTvXv3YkBAgKycnZ1dsfCtks1A+VbpM0XnqJUrbvvFmN8szW9paYnNmjV7bV1syvi06EkQBIyIiMCPPvqI2byRkZHYuHFjnDJlCj548AALCgpw/fr1GBsba5ReAQy6dunSpVhQUKDJRzQ/pZfnz5/jhAkTjPKQtP+urq44ceJEfP78uUhW8G04Ojqin58f1qhRA/v27auIG1M/NdlJefPGjRuKMuT48ePYqlUrLFWqFJPfhBAsLCxkeQ4fPoyurq6KOsbNzQ1nzpyJ06dPx+HDh2OPHj0wJCSE9WfEiBGqshPRYCuqjYf/lGxlKT75r3bt2tijRw9NfNK6KL/zc8TzWbt27RTHbg7vAgB6enqKfPauXbsqlrOzsxO1Exoail5eXujv7/9G4xdqOKNt2Nvb49SpU9kcSmMU5cqVQ3t7e3R2dsahQ4di27ZtsVSpUqr0SghBa2trTV1erly5N+YnODg44Lvvvivz4dXm29fX97X7okaTxmgJETE3Nxfj4+NV7aD27dvjF198gZ07d8bKlSsjAMh8sY0bN2r2QeqXSGNaPj4+uH//flGdV65cwcDAQJaHzq2DgwMGBQUxHS6Nme7du1eRPmrUqIGXL19m+dLS0nDLli24ZcsWzM/PR0S5jTZ37lwsVaqUUd9bLU2KSz7OqxXf9/f3x5iYGIyOjsaqVatirVq1MCwszCTe7Ny5M/r5+aGtra2qTa9m7/N5rK2tsVWrVnj48GE8fPgw+vj4yMbD8645ceOixkRoPZ6enqKYGyFEkc5MlWcNGjRAJycndHZ2LvJaiilQsvj2lsCECRNwwoQJmkGWUaNGKQZZKaHpdDpFQ+T06dNYpUoVRjQ0OCaFjz76iNUXFRWFp0+fZmnLly9n5dPT05nAlTKvGuOqMXNRBQHtQ1HaI8SwgDds2DBWvmzZsmhhYaFY96hRo/D06dMYFBSkWa+1tTX++uuvmvjmmbVMmTLMAOD7rjSf0n699957+OzZM0REXLVqFauTn6dLly6J6oyKisLly5cr9kWaJqULtTHw7UkXa5WEKSEE33//fUQ0OCA6nU5TKQ0dOpSNjxDCAkGmKjktelL6ffDgwbhixQqWp3///iIek45fizZ1Oh3Gx8fjkSNHTKYJKZ/xY1cbX5MmTVggqai8xrc9c+ZMXLVqFctjZWUl6guFfv36qRqs/GdpaYnbtm3THDufPyoqCmfPno3lypUTGclFHYvSJwgCxsfHi4L0d+7cwYoVK8qMBRcXF9y+fTsiGozOcuXKqdKng4MDjh49mv0WERGhyEfvvvuuSfMSFRWFmZmZonmgbfn5+bHx8jB79mxRsK0oOKMbCdTKaM2zlC61dA6vq5Tq03KatehFiT6N8ZgpQPWbUj+dnJywd+/eePjwYZPaU+PpNyGTlXiC/00QBCxVqhSWK1dOVf/TxTe+L5UqVcJOnTqhTqeT1WkKrUVHR8sCwtJ+xcfHq+JTEAT85JNPVOdQbfGN4pq2VRzzUBxpSrjz8PDACRMmYIcOHTT1GCEEvby88OjRo6Kxvnr1Crt3767KY7Ttbt26iRb5lXCtZBPy+p2O79NPPxVtjOLTjenpxMREfPbsmcnz4OfnJ7MXaNr777+Pp0+fVg3E8nmHDh1qEt+Gh4ejr6+voi7avHkznj59GiMiIlT1EO3zrVu3ZLjkcSrlU6ldIQgCNm/eHAsKCvDYsWNoZWWlyuuCIGDt2rVl9QmCgMHBwdi+fXv2m9bYIyMjsXLlyqKxSz9jfG9jY4MjR44Ujf/06dOiQJCSHWWsbmN2FyGEbRCg8tre3t4oParZDVp/a9E1BSnf02/MmDGq9KCmp6T2oL+/v6ycqfNDv+7du+OtW7dYeTUd9+WXXyrSgxJ9mqITaLmEhAST+6ul64y1pSYntOaS53tBEPDUqVMsYKVV7nX4VslmkPriRdU5anpk+fLlRuWglv3yun4zIYYA3qhRo15Lp9JxeXp6yvTYihUrsGHDhoo6VWkOpf6AIAhoZWXFNggUhbcADIHzPXv2qPIR3269evUwLCzMqG9F8yvRUXh4ON68eVMkKywtLWVjsra2xs2bN+OoUaMUF0aL8inJTUtLSxw9erRIfv34448in4MfP40LXLlyRXPTpZSnpXikMlENPvnkE5NlP28rS+lFaf6aN2+OKSkp6OnpaRRf8fHxZsduTI3PeHh4oIWFhayvNjY2OGzYMDx69KioHN9He3t77NChA+sDD8UZv0hISMD4+HiT9D6NB9HfaIyC3zC4cOFC0RxJ21OaC6XfnJ2d8dSpU6/tJ0j1CN/G2LFjERFxx44dMjkqHTuNz5jbFyX+of9XoyVKZz179lTlE2k/+d82b96MiIjJycno7OysygtSO3nv3r1sM4sazu7fv4/e3t4my4mFCxciIop0uFIZvV6PU6ZMwRcvXuDMmTNREAQMDQ1lfeNtrX79+hVZH5giQ/m50dI5UjloY2OD3bt3x6ioKE3elMqXihUrijap8v0x1m/aP17OKNF/y5YtFfui5Feq8ZGfnx8uXLhQVQ5+/vnnqrqTEMMGqW+//VamE0yNb9PftXjFXChZfHtLgE4mvwrPw/jx40WEQoMS1tbWzHChdUyaNElW/vnz52yVPC4uTrEN3kHhd1MhioMGMTExjOko4YeHh+P58+excePGLE2v14uCFVTBU8aoUKECbtiwQZHhHRwcsFOnTrITNwCGIH+1atUwODhYlubp6YmEECxbtiyuW7cOExISUK/XM5wRQjAkJITtXKCGKi98BEHAM2fOiJwIKry9vLywSZMmLK+/vz9mZ2er4puWd3NzEwkqflc1NYSNCb+wsDDMyMjA+vXrIyGGVX1+nngBy6dJ+6KUpkQXWuUoTUiVAiEEXV1dZTs+N27ciK1atRLRLoBhd8snn3zCgiU6nQ6rVq3KBCdPS3SXFj2106dPH7x58yaePXsWAwICjCoMcxVky5YtsXTp0kiIwXGcMWMGrlmzRrQDR9pWUFAQTpgwAW/evGkyPhER69evbxSfNN3UMej1eqxZsyb2798fO3bsyPjAyspKdLJo2LBhMjreuHEjo0t7e3scOHAg+vr6avZFyZk1Ff8BAQEiPvDw8GD8qTVHSk4Z3xctY4aO7Y8//sARI0aI8jo6OmKbNm2wTJkyLC9vNP/2228yPpJuTNDr9eyUiJubm8wojY2NZfMulUP0t3r16uHSpUtx3rx5spO4NjY2sl1dxr7y5curppUuXVo1TW2HPa9zpLrKyckJHRwcjM6dOfRSXOVM5aeyZcvi+PHj8c8//ywST0vldXHJZDqW0NBQ3L59Ozu1SfU0nQNbW1v08fFRrJM6srRcWFgY2/VIZR5Ph1LaBgBVHrWxsWF1q/FnUehWiX6cnZ3ZJz1dSTc1TZgwAQMDA82ah+JIo3QuPQ3L48PDwwPLlCmDACBbUKFzyG9u+OijjxSdYgcHB4yIiBDJkf79+8tkuzGbkNfvYWFhOHToUE0bhtKKm5ubTF7QflSvXh3r1KljdB6CgoJEi3zUZqDtUJuiefPmInz6+vriunXrcMiQISKbQnpCV9peaGio7JSMFKf37t0TnTD08vLCUaNGYVRUlGgHuZ+fn4ju+I/f4c7PrZeXF5YvXx69vLzY+JYtW8ZkB6ULQggmJSXh3LlzWV6ejvj5kfLb0qVLMSsrS3Hs9IQ8tTc++ugjXLVqFZ4/fx7T0tIwOzsbs7KyZBtTlOSmTqdj+mrdunWyEx12dnZYqVIlbNmyJXbp0gV79OjBThxbWVkpnqgsV64cVqxYUVOPmypPCCHYvHlzfPLkCW7cuJHhlp/X2rVrY2RkpIiu+QURJbrOyMhARBThXtrfwMBAHDt2LCYnJzMekzr4SrqD2sKjRo2SlVPDCR/QUZojmsbTiyAYFlg+/vhjUdC8SpUq2KlTJ1HggadP6h/5+PjINmzQ+ZPSKv2oPUQIwS5durATMIQQ7NevH/bp00dx1zo/D/zn7e2Nbm5ubFxdu3YV3SLg7OyM3bp1k536UKIT6k/6+Phgw4YNMS4uTkZjjo6OqngxhW+VbAYlX7woOkcqC/h5T0hIYJtGi2K/mOM3U5m8aNEibNGiBctLFybN1ak8P0j1GD9ee3t7Rk/Vq1dHHx8fdHR0VOSVotiOdnZ26OjoiA4ODlitWjWZfyYIAi5duhQvXbok4iNpPETKD7ydrMTbNjY22KJFC+zdu7dIx4eHhzPbjOJW6aMBU2kfnJ2dZbEWS0tLrFmzJtasWRO9vb1F/W3UqBF+88032KdPH3ZKgdLAhx9+iIiIKSkpIh3u5uYm85kFQWBzxp88c3d3l41dy0ZX8yG0ArO8HaFkKyvxktLmm7Jly8rqt7GxYbqDz1+5cmVFnjcWuzHFF3B1dZUFw/m2BcGwyeb8+fMyWcHjg/7G+zlFkRV8e9SeMDV+Ie07jQfRdBqj4Bff+EUEaYwiNDRURgPW1tbsBLaHhwfWrVuXtenl5cX+Nlc2Uf1HPz7eJQgC/vrrr9itWzfZHHl4eGDbtm1l8ZnX6YsSTikOeDtEGr+guKV+5YsXL/DJkye4bt06zRsvqJ1MbXw+zcnJifE1IQRjY2MRETE1NVWkr43hjPc1+dOy0vFaWFjgH3/8gZUqVZLxqRL9OTg4sJNc1M7ibS0le06n02GdOnVw0KBB+Pnnn8ti0mXKlFE9hRgeHo69e/eWxYtovXSelGIuUrstIiJCND5j8qVZs2Yy/RIYGIiLFi0SHRChvMPbdDw/WVlZ4ZQpU3D37t24bds2kd9F+6oVNzbFhrl8+bKiHFy0aJGijJfynyAIuGTJEpSCKfFtJdvclLUUU6Bk8e0tATrJSotv169fZ6cInJ2dceXKlUzRLlu2DPPz83HlypXslJCFhQXevn1bVs+yZcuQEMNpIiWgzgghRJYmFUCCYNgFsGjRIlYnv0ujdevWeO/ePdGJAkEQ8OzZs/jxxx+zfN99952IUem1BIIg4Jo1a0SnP3hn8vLly1irVi2m3B0dHUXtLFu2jPX94MGDTGEQYrgChuJp8+bNDC+Ojo7o6emJgiBglSpVmCHJj5nWO3PmTPbbihUrFPG5bNkymfHTuHFjTE5OFjH2w4cPRQFvGkDU6XSy3THly5cXzT0PvICVpvF9UUpTowutctLFN51Oh3PnzhXVaWNjgzqdDqOjo1Gv12NAQADOmjVLVufZs2eZQ8U7Dzwt0TqpwN2xY4dsDGpGgVaaVhkqeHNzc3Hq1KkoCIYrZh48eCAzdqVKg3516tTB77//Hp89e6aJT0QUBR6U8CkNLvv5+YnwCWAI/qn1x8/PD7t06cIcQmoUE0JE+KRAeczHx4cZalK6Vhq/mvNnDN90Z5GVlRVu3brVpLlVas+UuSeE4NatW9mj3h9++CG6u7uLDD86R3l5eThy5EiZQ8PLa6U5qlixIt68eVPEY4WFhfj555+L6uKvY5k1axZ26dJFtoDN57ezs8OOHTuy/1Na7dChA548eRIvX74sOqHXqVMnFASB9Y1fFOFxRAjB48ePq+KMN3T8/f2ZXKU6R0lX5efni+jaVFoxhWeLs5wpMoHn69jYWFy8eDEuWLDAKE8ryevikMmEEPT19cX09HQmkyktFBYW4owZM9DCwkLk0Ejr5BffBEHAwYMHszoSExNxwYIFIp1PaZwQwoJF9PSolZUVuri4sP5Jdz0XBdev+/G6gqfbos5DcaTRvly8eBEHDx6s2l/qWFIHRhAMu3/pHFpYWODKlSvx3LlzLN3f3x9/+OEHWV+OHj3KrqVTsy216IzX76NHj2Z4VLJhUlNTma3EX926bNkyvHHjBnOcBUFgAUAtnDVr1kykA/l+zps3T2RT8IF5vk7epvj4448Vx07LffDBB6x/AwcOxL/++kt0/bAgCPjuu+8iIYQFmCjOaDmertUCgUq0L6VTqv9CQkJQEAy7VKmdzTuCKSkpbOy8vUT1h1TG06BxfHw8DhkyhP3eo0cPEX/zV+dp4YyW+eqrr0RBBzV8ABgCHPQ6Hx54Gfnzzz/LZIWzszMuWbJEUYZQfcPrMen/AQzBZBqgEAQBjx8/jidOnJDZcJTHEFFE19QXUaPrtm3biuhh7dq1+Nlnn4nkIT/PtWrVwoSEBPa7ElC80KtfqY3G8x6t297engWapDTm7OyM/v7+inJHp9MxWpHWSYjhNojMzEzWJyrnefqk/WzQoAGj3yVLlrDrWRMTE3HChAmidqmcoH4rlVGUrmk53qaQBuWonBAEgY2B2iLz5s3T1A1qdKLFn/RvKysrbN26NUvbsGGDIl5M4Vslm0HNFzdV59A2hw0bxhYWpDTo5+eHo0aNwi+++EK1Tp4Gi+o3K9VJ/Wa1sZs6Pik/8PqB5vHw8MDk5GRRmtZcF8Uu4XkFEbGwsBDHjRsnok8LCwumM6g/wy8avP/++6L6KlSowOiFztOwYcMwKipK1DY/fw0aNGC/8Sc0eT324sULXLJkiWiTLwBg+/btcfHixUgIEdkb1FYXBEF0gu/EiRNYrVo1ls9Y2meffSbS4bzckspvGheg5ZUC/DVr1tS0IYviCyjNn5KtzPO7dP62b98u8vWcnJzw66+/ZrSWmprKbDepripq7MYUX4DShbu7O86bNw+nTJnC+k/tQEIMV2aPHTtWNL74+HjZqSKpn5OZmfna8QvpJj13d3ecPn06Ahj8WH4RlMc9/T/ld34eqJ/s5uYmilHwfoqLiwvOnTuXLQbTBQY6Bv6EJr2a2FzZRPs1ZMgQEc5oXKthw4Zsw+zkyZNlde7cuZPZyNJ+mtMXaofQzZfS+a1VqxZ+9913OH/+fDZHhIj9Sgo//vijzE7g7QXeTuavQqdjePHiBdMjVE7Qa55NxdmUKVNE/E31DZ2/Tp06oaOjIwqCgO+88w4SQnDq1KmyWPTQoUNFJ1Z53JQuXRpHjRqFf/zxh+KtOILwd1ySArUleTt+5MiRiPi3TchfJ00IwTVr1jD7hqd5Ov8TJ04ULSYBGHzqHTt24MyZM0V8zW9y05IvdNMmHQPVyS4uLjKZreR3Sa97XLNmjagt6nep6XjerzSFj6jfyvfl/v37zN6ztLRkN3zQOh8/foyTJ09m9GNvb694q6AWH2ltjNOSyaZCyeLbWwJSQuNh0KBBTAnRkxb09M/Zs2cxMTERExMTsWXLlkwQVqpUCRMTEzEkJIR9dDcIIUT0O/14AUPrpB+/k0EQBOzYsSMiGhiNEjc9EdawYUN2n7dU4NHx0auW6taty9q0s7PD69evs512TZo0YYaLhYUFbty4ER0cHFAQDFdSUUNm9erVeOrUKZGSPnv2rAiHo0aNYu1ER0ejIBjet5GOgRpPgmAIZNM0XuBSnFDnMSAgQIavxMREbN26tUhglypVCnv27CnCJ+0/L1zbtWvH+kMNON4YpEpGOkZewErTzp49y9L4MfD91Ov1mmNo1aqVjCZ4ZUSvkZDi9P3332djXblypagvfD+HDx8uUupSWpLOxfnz5xXHRw3huLg4tquWd8qqVauGcXFxsh3P0s/BwQFDQkJEb4INHDgQCSHYs2dPxoO0z7wTK11ApA5FkyZNVOeI8rUWPvmgFo9PJeOgdu3ajHekfeGVpxI+ERFfvHgh4jElXFNjPy4ujgV37O3tMS4uTvZFR0fLcGxpaYkxMTEsD30XYfDgwbKxSw2kcuXKMflQpkwZ9PT0ZB+PR749vV6PHh4eLF9iYiL27NkTXV1dGY74XY/SOWratKlo7Ly8ls6DnZ0dpqamyviBAj1VKJ03WufJkydFv/P5CPk7yE2Ns4oVK4reUUBEUf7Q0FC8cuUKu06Hr3vdunXo5eWFgiDggAEDWJqFhQXOmDGD9ZN3nnheBTC8K0D5g9dVUrp2d3cXXSvp7+9vEr1YWFiITjk5OztjQECA7JPu4KVODP9Jy6idHKxWrRrWq1cPK1asiO7u7myhk+clfmcaP2Z+7EryujhkMoBhR7dUTwuCYVPIihUrcOTIkaJd/nq9nqWtWLGCOel0TumO6vLly+OKFStECxM8f9K5pjyPiGzhTurk0fJUVtAd0HyQRafTobW1tdGTrqZ+akEYrXl4U2m8fUf/trOzY6fHlPpJf9+0aRMePHiQySi625IQQ+Dzxo0bIt0/YcIENrfTp09Ha2trJIRg06ZNccWKFVinTh328e818L/XqVNHdMqJykQ1G4YCPTGphJdp06aJ9I0WzsLDw0U6UCo/eZti0qRJovZ4XqFvi7i4uGjyEZ0HDw8PRZuXfm5ubtiqVSsRb0r1UnF8/OloLy8v0SlDfgxVqlSR0Uu7du0wMTER4+PjZYtivNyS4pra9FrAz5HUZ6H4pIEPab+oDL906ZKsXl5G0vddYmJi8NixY8zmDw0NZXruxIkTIpzw74UEBATg1q1bZacipfZT69at2fVK1apVw3379ol4jF5bpETXvLzm9RS17/h5oPQi1ZfSb9CgQbhs2TKRbKa8SwNiEydOFMlknu5mzJiBiH+/tcKnSduWylhal42NjWzDHw1IdunSBXfs2IEFBQWiDRpbtmxh8xccHCyy3yZNmoSEGE7mSN81okEfPvBPaYkvx/u/9No0JTlBfUBBENgVn0o0qKQnisKbVDcmJCQwvyIoKAgTExPN4lupX5WYmCg6mWiOzpHik6cJqRzg6eLAgQOqvFlUv1mtn/wbaubqVBpEpz4Lr1Ol/MD3k59HDw8PdtrK0tISAwICFP0Gvk5XV1eZ7YiIbN6aNWsm4i0a/C9fvjwmJiZi27ZtWRpvc1JfVxpnofP36aefMvlpzJcj5O9TJTzwuqBKlSpYWFjIcEN9iOjoaHz69CkGBQWhIAjYu3dv0fh69OjBTrb7+flhYmIiewea0gmAeEOEXq9n6dITUPQrXbo0u11GOiaqw+nJZ2pHSm9hKF++vKIfwc9lcHCwLJ0H3s7i5R+VZ/yiDyKKTnlu3rxZ0c87efKk7C0irdjN6/gCVlZW2LFjR5l99vLlS+zfvz+jHZ6mKZ0VFBSINhxJ6czGxgYbN26syp9UVtA6leIX0s3DfPzC3t5etAgqlc88v/NX5dN8X3zxhaKsEAQBFy1ahIgouu5WaoNS/CilSevUShMEAdu0aSMbu729PYvtCYLhWkS1Ovv27VtsfaEypLCwULRR1Njn6uqKgYGBoo/6a1ZWVmzDhVQ3CoKAVatWxaSkJNHcUbru0aMH27RZrlw5dhqOyhMpzfO0Wrp0aZFOBTCcdE9MTGSnmEqVKiXasGBjY4MhISGictQ+S0hIYCehpVfxUvqS3tpDZbU01sLbknSjdlRUFCKK4/v8kyRNmjQR2TeUBinOOnXqhIQYTg1v3LhRZt9I+VqNJnj5Qjcx0DHw9El1vJLfRdcTaD1UplH8K/ldANpx46LYMHxfRo8ezfTWjh07ROX69OnDbhqhC8GEGE5rS28i6dOnj2I5ekuJ1L9V4mlzoWTx7S0BKaHxQN9i+OCDD9hv5cuXR0EQcP/+/YqGmNSALO5PyYlXSpMuFtDx7dmzBwkhohNfPXr0QETErVu3IiFEJNxbt26NiAYjlBDCdngKgsB2Zfbq1YvVJX2o8+jRoyzNxcWFBZwREQ8cOMAM5OTkZFbvmjVrWBoVelq4VvqM4UUQBBZsoAKLCvvy5cuz/p88eVL2zpN0jLyAlaYdOHBAtR/SQLxamrFy9KSSFKfbtm1jZVNTU0V94fvJ07JWnbRt/vF4vk4Ag/Hfu3dvtouaXh/D0+DRo0dZwIFX1FKj09raGtu3b49btmzBa9euMeOV8iBfL90N27ZtW1yyZAnWq1eP5ZEGQLp06YIXL14UzRO9DlBt7LxTw+OT5zH679q1a5EQw3UeBw4cwMTERHZCVCngMH36dNGu9Js3b8p4TImWKK537tyJhBgCwkognSNBEHDevHmiPDRwt2vXLtnYpTv5b926hXPnzkVCCAYHB6sGFLR4UImX+f9L+WjdunWi/vPymp8HABA5Hzw/8MDvEKZt0zp5R+/WrVuiBWSlOezUqZNsdw/vxM6aNQsRkeGMlmvatCkiIjNoqlWrxtJoQJYagfy1Pkp9oMGGDh06KNI1Ty/du3dHQgjWqVPHJHqh5fjFcLX5LMrcK9GA0vwiImZlZeGy/3+tFM/XvG4ZMmQIXrhwQTR2qqvVxmeuTNbSK1r8UFReof/n+ZPqSgDD24eIiLt27WJ5mzVrxuqiC3pSWeHg4MCug7S1tcXJkyfjvn37kBDxIrinpyd+//33ePDgQTx48CDbuUsIYb8dPHiQXVtL8SZNMzYPbyqN1xG8LKABq82bN+PEiRNlV/rSMVauXBmHDx8uu7Js0KBBiCjW/Wr2F68fzP3UaI3CxYsXNeUn3WylZL/wOKN4oTpQKj95m2LTpk2KuC4qH6mNT6obpHVq4coUnJoqv9TymzqG8ePHMz2p1Le+fftqvjmtJrOUbPpbt27Jgst8/6tUqYJz5sxh7fEykl7989NPPyEi4tSpU5GQv3cDz58/HxERN2zYoFg/fUuU5wmaRzpuGnSeNm0aK0NPAAiC4VSfEl2r0ZhaHnN5zJhM5ts7deqUjFdoHn7TiLRO/ipBpTxSe9jT0xPbt2/Pfnv69CmbP3rSlNpvR44cYXKeziulAcrDtJ88LfHlpP7vmjVrVOVEr169RDYFP5Z+/fohgGFxlj4DMHr0aDx48CB+8803CGBYaOnbt69J81xU2arGt1q6nxDx4pupOkeKT54mtHwBCwsL5utQX4DObVH9ZrV+Lly40KQxaKVROqM+IO0LHxxU4gfqQ+t0Opw8eTIePHhQ5LPQRad58+YxmzcyMlJWpxSM2VOmyAIpTanxgym+3IABA2QnjPmNPnQxguKGbuilpxiozJUGj4vbnpDyh1S/BgUF4d69e5EQgx9Lr+GjdiSPu27duinODW9Hqs0fD5cuXZLpLirPpItv/AnWp0+fqvp5W7ZsEY21YcOGWFBQIGrTFHlgri9AYeXKlWxDB1+n1OckhCj6OXx7nTp1Evk5iMbjF/QtQpqHj1/wODt48KDselie35UW33h5wcsKQRAwLS0NEf+O+VD8SMuopUnr1EoTBMNVptJ6y5cvzzbcCoKA9+/fV62T2lLF0RelGAW9KtYYTxqzC9Rk2KJFi2Q3qqjJEDXaNVVuKMlWU8em1hdj5TZt2oRSCAwMREEQ8Oeff2Z0RvUKr495W9LDw0Nm3/A+Eq1z8uTJiCi3byisXLlStOljypQponRevtCT7Eq+lbOzs2qalP+lMsQc+8ZcG4beVkU3AmnFUqQypCj9lMpPJZ42FwQogbceUlJSAACgZcuW7LcOHToAIsKGDRvYb4goKif9uzgBDQu3UFhYqJpGPyVwcXEBAIAXL16w32rVqgUAAGXKlAEAgIyMDJYWEBAAAABNmzYFAIC7d++yNCsrKwAAaNWqFfuNxwufBwAgMzMTAAB8fHwAAGD9+vVACIF27dqBt7c3yxcUFMTSqlevLhqPEq6lnxQ3ang5c+YMEEJg6NChQAiB58+fs/5QiImJgenTp4vq6tOnjyL+lca/fv16UVm+P3wdWmNQGiOfRudNilMvLy/2u6urq6gvfD9dXV1l+FKqk+a7du2aLA0AgBACdnZ2sHTpUhgwYAAgooiWKFCa4Me4f/9++Omnn6BJkyYgCAIgIrx69Qo2b94M7dq1g9jYWEBEyM7OhoYNG8rw8O677wIAwO+//w69evWCgwcPws2bN2HChAlQrlw5UVtr1qyBqKgoqFKlCsyePRseP34MHTp00By7lO4oPunYeQgKCgIAgJcvX0JcXBwsXboU/vrrL/jpp58Yr/EwfPhw8PLygoEDB8LZs2fBxsZGhivaFwqxsbEM1927dwdEhIKCAlnd0nIUYmJiRH/n5OQAAECpUqVkYx88eDAMHTqU/V6jRg1IT08HAIAHDx6w37XkjhINK9EyBSkfBQcHK+bj+0mhatWqsjSepwEAOnfuLOvfvHnzZP0OCAiAnJwcEAQBvv/+e+jTpw9YWlrK2vf29oY2bdrAhg0bIC8vT5RO+yPFeePGjQEAoHbt2gAAcOvWLZZWr149AADo1KkTAAD88ccfAAAgCALs3bsXJk+ezOQoAEB+fj4gImzcuBGioqJg1qxZkJqayuiap5ft27cDIrI6pSClF1ouPDwcfvzxRxnetHQSzVNYWGh0/rXA3t4eEhISYP/+/ZCSkgKTJ08GZ2dnlv706VP45ptvoHLlymz8TZs2lelq6fjMlclK6UrjVtJXPC7U8CVN5/kzICCApTVo0ID9TuG9995j/4+Li4M5c+awOXzvvfeYbL18+TIMHjwYcnNzYezYsYzWqlWrxso/evQI+vfvD0ePHoVatWpBREQEEEKAEAL16tVjX48ePURjkKYZm4d/Ko3KAsqjPj4+MGbMGGjdujUQQiA6OprlR0S4cOECfPXVV5CWlsbkMiLCo0ePQApSWlJKMxeM0VpYWJim/Hz//fdZPVo4e/nyJQD8bQtK5SdvU/zyyy+K7SnJdWNywhSZIOXN14WiyiJT+Z1PJ4TAuHHjIDk5GY4dOwYRERGyct9++62m/gBQllkUeJs+ICAAsrOzQRAEWLx4sag9RITff/8dBg4cyNqLiIhgNJGWlgYAAOHh4QDwt2yh/QkLCwMAg61GCAF/f39RX2g6HTeAQV9t3boVJkyYINJXBQUFgIgwcuRIqFKlCuzbtw82b94MgYGBgIgwfPhwGDdunKhOfuwUx7QdJXpT00dqQMsYo08pUHtAyiuEEDh8+DD89NNPUKNGDVm59PR0cHJygsjISHB0dJSlC4IAdnZ2rN1Hjx7Bpk2bWD+XLl3KdFx2djYA/K0HHBwcAADg1atX8PjxY1G9O3fuVLSJpOWk/i+1awHkcoL6gNSmmDVrFsu7aNEiIIRAt27d4Pnz5wBg8Cfr1asH9evXB0II5ObmyuwjNRuxqLJVy940xRYFMF3nqNmm7dq1k/kCvr6+LF9eXh7zdagvUKtWLUDEIvvNav2sUKGCSWPQSqO0Tn1A2pcWLVrI8vD9tLe3Z+ljx46FxMREkc9C/YiYmBhmh9y7d4+V0el0oAVSeSD9XYtGtNJ5fpDOn5ReAQDmzZsn8uUAxPGUihUrAoDcr6S8Vb16dQAAuHPnjqyPWn+bC/7+/jL/H8DgH5UuXRoADLx+/PhxkR3J9+PMmTOKdfP4MTZ/AADly5eHZs2aieqW0iKFrKws9n8HBwdVP8/f31/096+//godOnRQ1LFv0heIj4+HXbt2gYWFBftNSc8QQmR+TlhYmCjvunXrRH4O7+chKscvpMDHL3ic1atXTzS/tJ98zEcKPJ9K26N6jcYxdDqdbF5pHhr3MVc2ISL4+fnJ+tKuXTvR7x4eHqp1enh4iOp7nb5I0wghbJ4EQQB7e3twcXEBQZAvByAi6PV6cHFxAR8fH4ZTT09P8Pf3h4CAAGb30fyIyOSL9HelPknzFNVmMkVvapUrKiAiJCYmimQrAMBff/0FAAb7k9KZUiyMtyUzMjJk9g3vI92/fx8AAOrXrw8AcvuGQnx8PDRv3pz9PWbMGFizZo1i/6keV/KtaDxeze9SA2M2jFqauTbMjRs3AAAYHWvFUng5oCU/i+KHmYoXTcASeCtAusrLAz11xV8Ll5GRgUFBQajX69m1LE2bNsXt27cjgOEahZSUFLbKHRERgYIgYJkyZXD37t149OhR2ce/RUL74+HhgTVq1JA9ohwSEsJWw62trTE2NpbtKgsLC2NH+unDhADiXRjz5s1DQojoKoQNGzYg4t/3qfr7+7OV6Ro1aiAi4u+//8520NBr0g4dOoSIiOfOnWN16fV60V209P5/epydEIKnTp3CdevWoV6vx6CgINmD6TNnzmRpt2/fZjggxHCF065du1idKSkpom/BggWo0+lE9ywPHDiQpdOr3ATh73ud+ZMzfn5+rD8U6DV0/Gp9tWrVMDc3FxHFuxv48dMxBgQEsOu+6BhoP/38/PDixYuqY/D19UW9Xo/z5s0TjYGniSFDhojaozjld/ssWrRI1Be+n7/88gsSQtDZ2ZkdAadvtfB19u7dGwkh+M4778jS6GnJDRs2sBOFAH9fA6lEg/w1dfv27WP4fvjwIX711VfslKV0h0xQUJCIJi5fvoy//fYbEmI4pSWFdevWoU6nY/xK66G7LCwtLdnVrWr4dHJyEu0co/gMCgoS7UwRhL9PvindS0x3ilDZIuV7QTA8zizlMSkt6fV6XLhwoQjXNjY2eO3aNdnY9Xq96KQrIUT0bgAiYunSpUU7MKW0xO965sfr4uLC3glp2rQpLl26FAFAdr3GTz/9JOPVlJQU/Omnn1h9/DV8Ujly4sQJEQ18/fXXsn5SPMycOVNxDDxQeSa98mDw4MGyXZYUN3v27GG0JL1Ckp8/+ogx/Z1e90V31dEya9euRUTE8+fPM9lK09avX4+IiGfOnGGyXspHVO6q7TKysLDAxo0bM30lpRdCiEn0wpcTBAHr16/PeCkuLg4PHjyI48ePR51Ox04JEkJw/fr1GBsby8pVrlwZf/jhB9GpqBEjRiju7FPSx0r99PLyEu3U5OdBr9ejra1tsctkejXVBx98oKhX6NVMNWrUwH79+jGaoPKOEMPVzjy906tMCSH42Wef4fLly5m8FQRBxJ+LFy9m5ZKSkhDRcCqB5qW7fSl9r1mzBu/fvy+aewsLCzb3586dEz0aTa/NJcRwwovuzg4PD8exY8cq7krLyMgQ7fCWplEaVJuHN5WmZFNkZGSw0yAbN26UyQlapkGDBuzaOSUapbJC+nuNGjVw+fLlOHHiRAQw7PZfvnw5Ojg4ICGEvQ3q6uqKkyZNwhkzZog+/s0GWueaNWsUaY0ClbsAwGwGXn5SG4anCS1+UMIL4t9X9hJC2JsxFNe8Pfzjjz8igOEqRC3bhra3fft2xfHRMYWFhSny5tWrV7FXr16i68FXr17N7O969erh0aNH8fPPPxfhFAAU9RF/Rdq6detYOUKIKB/FN+0jIQT37t2rOUc8rvn3+ZT0h5Ksk86Rkk2P+PdbUePHj0e9Xo9ly5bFFStWYOvWrUX0zO8w1ev1zCY5fvw4IopPlAuCgOfOnRPRxKNHj9jVjgCAUVFR+PLlS9nOa7ob/fz58+w3np94O6xRo0bMdhcEgV0DJcVV06ZNMSUlhZ2+pO8uCYKAe/fulcly+p5M5cqVZTzWqFEj0RVHfHv0/+XLl5fJZJpnxYoVqjKEnyMAkF2zzM+Dr6+v4vXShBhuAFEqS3UcLycQDe/DEWJ4Z4j3hSjfqvWTLyf1f6kNyF/NSOeWvoVpZWXF5ISU30JDQ5luPHPmDCL+7Xu4uLjg6tWrWd7Y2FgRv9ETc9QPkvqA5vCt1K9KSUkRXaVrjs7heVPL/qRpvr6+7L0mWpafd0oPRfGblfqJiHjhwgVF+7oo46P9e+edd2R90eIHevJm5cqVGB0dzejR0dER09LSRDY2tXn5K/D4Ey088L5HSkqK6A1W+vXu3Rt/+eUXhkfeVxAEAY8cOSK64l2NH6RAnzug10EqyfLIyEh2AkyJLry9vUV+Fx9nkY6PyhBPT09MSUlBX19fpuP4NHqK4ttvv1XUR7wsuH//vuI7lu3bt8cePXogAKC1tTW7EmzIkCHsmjn6WVhYyK4VmzBhguiqT+pTawHPE3z9Sj4Z/9TJ/PnzVfmMzp+Li4tITjZu3JjpKiXdYqovQMsNHz5cU/fT8fFvTAEAdu7cGRHlJ/uUyqnFL6ifR9OU4hd8vEEav5DijOcV2pa7u7usn/Tmonr16onaK1u2LKNBer0dtVFo7IaXMTTuGBAQYLafQMf22WefycaekZEhurGBvsOoVCe1eRwdHc3uC8WZUowiIyODnapq2rQpS/Px8RHFTHgZYmlpyfQmf10g/4YX/Xr27Ck6nSiNne7cuZPxLL1NZcmSJSK6pjTP69Tt27eLbGhBMJygT0lJwVWrViGA4eawlJQUlqdatWoy25vaZ1Id/t577yEhhpNllI/49xH5j5etiMho7dChQ4zOypUrh4ji+H56ejqjaW9vb5l9w/tI9N9z584hoty+kcosGq+j/fv+++8Zv0jly/Lly2U0QW12Jb+L6gBafvTo0WwMUv+F+l3UNlSLG5trw1A9fubMGdVyNCZEr9K1tLQUxXxorMjT0xO9vLxQp9PhuHHjZDEhJfmpJrOKAiWLb28JSJ0PHugilPQ9mVu3bmH16tVFimnHjh3MmLx3757obvnq1atjSkqKah+UhGhkZCTeu3cPlyxZImJcGoTl66TXiPHvZCm9+Xbz5k22SMLfubpq1Sp88uQJezege/fu7EFMQRDwu+++w1OnTjEjZvz48UgIwZo1a2J2djZb+HBwcMDq1aujIBjuXv7+++/ZtYPx8fHMkXB2dkZCDFcrJCcnY15eHt67d08k9Pjx3bp1i91za2try4xeT09PVnbTpk3Ytm1bFASBleXvv23Tpg1u2rSJXXNCCGEOOX23ghB5MBoR2fgcHR2xYcOGrM7SpUvj6tWr8ddff2W/RUVFoSAIbNx0jMnJyczBpwaYdPxKY6C0RnHKj4H/pO3l5eVh8+bNFXEqrZO+0REdHY1t2rQR0TVf5/Hjx5nio+OkdfI08eDBA9GDqrT98+fP44MHD5jDwd9j3rdvX0Xe+P3333HQoEGyAPutW7fY3/Xr12ePBzs7O2vi8/r16+yKFGlQRwufSm+80Dr54KwgCGwhr1q1arLxfP7550iI4f2UzZs3i5QhXzcNcoeGhmKrVq1UaaJJkyYiI8nKygo//fRTXLt2rWjsc+fOFfW/TJkyuGnTJrx37x7m5uayaw0jIyNlOENEbNCgAZOV7733HntDgHdmAwICsEePHsz44XEyceJExfnlH/2lbw9S3PI0P2HCBBn+pXNEjdfatWsrjoGHEydOMKdaSgcVK1ZkbeTl5WFMTAwKgmEDBV8nDeD37dsXq1atKguc8EYMImLHjh1FbdHFNypf+HdLqGFEAxFK77PQck5OTkxX1a9fX/buoZTfqVNtKr3Qcp999hl6eHiI6N3Ly0tWTmo4bdiwAcuUKcOM19GjR2NOTo5IT0nnoH79+iL6NCYjq1WrhoIgoJeXl2xBtbhlMn93upJeqVatGtMXfLm0tDT25p4gCNioUSPRHFE+DggIkNVZq1YtGb1aW1uzDSA8f9K76B0dHWV8VLNmTdHcd+rUCVu1aoWEGIK/9Jo02i6lwcqVK4vmiabxMGfOHBle6PwlJydrzsObSpPSAZ1bei0xve6KlxNSh23u3LlYvXp1GY3yc0h5lNqAmzZtwm3btjGdVBSbkOcJGjAKDg5m+KQ2ISGE0SftG11Ulc47tU/t7e01cUZlKJUhUn7g6ScgIECkj3i7gb7JVrVqVU2+pTZhjRo1FMfH49sYzvh5oVcY0reBlB5vVwI+kDp9+nTVx7+pnuPfHeNlltIcScdONzjo9Xr08/MT6TYtnPHtffvtt+zNng8++ADv3bsn2lgnxdnjx49x9uzZIn1F9S0t88knn+C9e/fY9WL02slatWrJeCU/P1+E0zp16uDx48dFup9e0crruSpVqqAgCBgSEiJaEKS4pnJSOme8H8DTNb0KThAMi+bSftauXRsJMVwxz9MDnde8vDzcunUrduzYkflYhBje4eRtVF4m832S+iy8DOFt3n79+rG0Hj16MB6kv9F3t0NCQmQ2RV5ensinof2gvheAYXFv06ZN2LFjRwQArFWrlui9XADD4rxSP3m6rl27tsj/zc7OZnPWuHFjJuu8vLxk8yCVE15eXmzDKO0zvdp0wIABTGfyi2/e3t4ifqN4qV+/PiL+bb/RoLA5fCuVkVJ/2xydQ8tSPV1UHy8oKEhkBwKAWX6zkt9I/Q36No854+PfI5XqVC1+oLbzmTNn8N69e8yfI8Twrk5ISAgKguGtHWrz8s89UBkihWbNmonkPLWxeX9RamtJNzvz9ELzK/EDDydPnmSLIUOGDEFvb2+RXcXXzy9C0bfQKG7o+zZVq1bFnJwcFmehGzDo+Pz9/Rn/tWvXDhERO3fuzGxCPo3K6fbt27P+Ki2wEWK4BlMpjfdh6N/8J5XLtD6tPFKbUIsnqlatKior9cn4N+yktKbE77Vr18YbN26IFrrDw8NF11Ka4wtQHU4IUfQFlMrRxS36VahQAWfPni0an1p7ycnJLH4hnUvevpTyLr9QaAxnVCZXqlRJVK506dKifnbv3l1kZ/D9pM/UtGjRAu/fvy/Sf7xsatGiBbPV3nvvPbPtff6KWqVYGH2Dypic7NChA5sTc/vCt6PUF+pv0YUM2he6gNqmTRuRHSIIAtPFO3fulPE0z2eEEJlfydtMNE7m6urKZEajRo0U4yX8IvjPP/8sk5Hbtm1DRHFMCxFlOlxNtvI6/PTp00xufPfdd6rj4zf+UN1O30vr16+fiM4QxfbNt99+y+rp1q2bLL7Px7Hpxo+YmBhF+0aJN/fs2SPaMNK7d29RbJhuTKZj4GmiatWqLH4hnQf+GQlB+Hsjbvfu3VV5t2LFippxY3NtGLrpXKrHbt26xXSunZ0dbtq0iS302tnZmRXf1vKDXgdKFt/eEqDMuXz5cjx06JDoq127NgqCINo9xhsXUmXGM4iWUSL9pEaMmgEkrZPWK83DC39aT9OmTVkgx8LCAqdOncryN2zYULTYtmDBAtHud51OxxZoQkJCMDc3F2vWrImCIGClSpVw1KhRsj6r9bu4PyWjUAunRa1PyQiVju112vtf+UyZb1dXVybQra2t8euvv2Zper0ef/jhB0UePXbsGDt5xtMp37YxR+HfmCNCxAHqpKQk5rAZ65Oac1PU9k0du1p7xtqUGn60vQ4dOojkkJOTk+ydvYsXL6KTkxMzAMLDw0VzyMvN150LU5xHtXJK8lVKf9J89P+VKlXC/fv3Y0JCggxfAwYMwEOHDrHNBLyxS3fN0Te66JtavK6iMrxMmTIiXfXkyRNcsGABxsTEiMZuDJ9FoRelsloOO1+3IAgYHByMu3fvVgyK8/Wpff8WT78u7anRnRL9KNGTFv9p1WsKzWsFVejHB5aok0TtIrW5/7fxX9S5Uho3IQS/+OILtnkpJCREkwaVgllK6cbsDaUyxuyRt+lTCsiZSxOm4Mzd3V22gzg1NVVRznTt2hUvXbrEdsB369ZNlG5nZ8cecKeOPiLiDz/8wMYg5VE1m7GoYzcHZ0ptm0pnWv6MlB9M8V94vvHx8cHCwkK2+zw0NBRHjRqFhBg2wG3ZskW0wC0IhrdupRue1NowpZ9q5fh5pfD06VNRelHo09hcXblyRRQQKigowJ07d2KXLl3Q1tYWZ86cydK7du2KW7ZswcGDB6Onpyd269aN2VaOjo6yIL85vKRWPiEhgdkUU6ZMEW0+ULOJTGlTLW3o0KGixTdBMLxLREGKl6+++goBDP6ouXwrtYn+a7rqv/JpzXvdunVl/Ni6dWsEANHtC1SGSIE/ic+fSpVudON9EVo//SwsLLBnz54i2rt8+TKjF0EQcNy4cVhQUICnT5/Gvn37sg0D9NQFX59UviqN3xQ/hB8fX46+g8af1uPTJkyYwPAwYsQIzMrKUtzgbWVlhaVLl8Zp06YZlQdSW0ir31py+U3Z9Ep1S/vwb/D46+jxf8rXMYYzLZyr1adFH6bQ/v/FT8n/o7/RBQp6ogpRvKD+7rvvstsUeNyaYtfx7Zk6N4JgOFnLx7TobVn16tVjeaS2N5WtiIirV69GQv7eEEXlt06nw08++QRnzZrF+sUvrjo7O2NMTAy75Wrw4MEiXFlbW+OVK1cQ8e/4fnx8vAgXNMYtCH/H9/nbnZToVY1f3iRvas0DfYeRwv79+0Un5aX2zb/Jb+biTKmc1qfT6WQ2ghRKFt/eEjBlQj/55BNRflOMDzXhVhQjxhjzvQ5DGwvwUGL39/cX5Xd0dMRevXph//792cmxf5OpzcFpUeszFXcln+nfF198IXO2BUHAOnXq4NSpU/H777/HmTNnYps2bUTXh9FrAUwx/ouLJl7n27NnD+7YsQNHjBjBdoTSnXX/C3T03nvvyR5otrOzwwULFohwLwiGa147duyIn332GdvZJQgCzp49m50WUprD4uinubRgbl5T9YSa/IyPj0dEw9U2hBhOlEp1ldTxluoqqb76p2lO2q4gGK404x8R5k9cGCv/tvD0m6C9f3os5rRHCJGdiKxVqxZ7yPpN8u/bgBee9xISErBx48Ymj68oNuG/jYPixuebts/4+po1a4YpKSmihZvw8HCcNGmSrF01542vk3cShw0bxh4cp+lq9qEaL7zJ+TW1fmlfitIvU+dSKvfXr1/Pbkdo2LAhPnr0iG2osrCwwB49euDPP/+My5Ytww4dOuCnn34qsyteh07U8vGLM2p6szjnh/c5pbetPHv2DLOysth1ckqfhYUF/vbbb/jRRx/9o7RkbDHhdb/r16/L/IEdO3Yw3EjxIu2bOXz7vy573/aP8sOqVavYldX8R09s0PlcvXo1IiK+evUK09PT8cKFCzh8+HBRfYQQjImJwczMTEYv0nopvfCnp7Ta1ul0GBkZyYLMlI5sbW3ZtYbScRUHXdWuXVt2pWXDhg1x9uzZ2LdvX/T39xel9e7dGxERU1NT2TVmdKxSWSoIAqubxxF/NSOAYWFy5cqVomvD6Dd06FAEMGz+4zcH1q9fHzds2KA65/+WTf82+EBF1c//tmwqkYn/3sfbTx988AEKgoADBgxgOlF6uvvly5fMJ3mTuhrAIDOioqJEMS16RSOViVSHqy2+DRw4EAkxbIagcp2e9KVyi7bXv39/2Y02hMifDQEADAwMxPj4eHaKUQmvSvH9Dz74QDW/0rz827zZp08fXLRoEc6fPx+7du2KFhYWb619Y25fTNEXUl/D6JoPYjG9mFoCrwWCwmOXUihTpgzcvn0bdDodTJgwgf3+4sUL+OqrrwAAwM7ODl6+fAk1a9aExo0bi9Li4uIgLi5Otf4tW7awRwarVq0KS5YsAUIItGnTBlxdXeHcuXMAANC2bVu4fv067Nq1C54+fcrKE4VHx80BovAwq5eXFyQmJsLUqVPZI4rSfGqPdNrZ2UGzZs3YQ+6pqalw5coVsLKygpo1axapb7m5uXD8+HEAMMxZYWEhNGnSRLWegwcPAgBAWloaXL582ShuoqKioG3btortzpw5E3JyciAkJATi4+NZ2u7du+HkyZOih5OV5pk+7FlQUAB3794FAABnZ2dwdnYGAICAgADFPqWkpCimZWZmQnJyMtjb24O7uztcunRJcQ78/f0hMDAQXr16BWfPnoXc3FzV8QcFBUFBQQFr78GDB3Dz5k3Iz89XLWMuEELA0dER6tWrB9u2bQMAACcnJ8jMzFSkQYrbCRMmwJgxYxgPjh8/HgghkJiYCD/88APk5+dDmzZtoHLlyortUppQAorrJ0+eqOLTzc0N0tPTizROInl0FABg0KBB7JFhAANdHDp0CI4dOwYFBQWaOJCChYUFhISEgLu7OwAY6K+goACOHj0KR44cgYKCArC0tAR7e3vW99q1a8OlS5fYA7C0r8Z4RJqHjk+KK1tbW2jUqBFs27YNCCHQr18/mDdvnuJj6NHR0XDkyBHYt28ftGnThtWllNfZ2Vn0eLkWuLq6ih4f5vmysLAQvvnmG3j+/Dn4+/tDSkoKEEKgb9++4ObmBuvXr4erV68CIQTGjRsHAIZHx1evXs0eQlfChbS/SqBUTimvu7s7PHjwAOrWrQsnT55kOFRrg7bP6yoAEOkrCgsXLoTU1FSmq4zRi5JMq1y5MnzzzTcA8PdD2ZaWlhAQEAB//PGHCHc8jBs3DvLz82HatGkwdepUePXqFRBCwNLSErKzs9lY+vbty2haC0zhabU0APNl8vnz5yE3NxdsbW2hQoUKkJmZCQ8fPgRPT0+oW7cuzJkzBwAAGjduDLVq1ZLVU1BQwOgMwCDLAADu3r0LP/30E+Tk5IhoQ4s/BUGA5s2bQ9WqVeHp06cwe/ZsliaVP4IgQLt27aBChQqsH9K5p3MIYJjn33//HQAMD6N37NiRPcju7u4OqampoNfroVq1alCqVCnYunWrbO6NzZEaZGRksPkwN43WX6ZMGbC0tASAv+dWOocABlqncyEFHpcuLi6QkpICCQkJsHHjRiCEwIABA0TzfubMGXj69CnExsZCw4YNTbYJr1y5Alu2bAEbGxsYOHAgXLt2DdatWyfSDUp0ER0dDQ4ODrL6CgoK4MSJE5Cfnw8hISHg7e0ty8PPQ3Z2NqSmpirSW1Ftivfee08xjdLEgwcP4Pr162Bvbw9Vq1YFAIN9Q9t+5513TMLZnDlz4OnTpzB27FgYP348PH78GDw8PFg9er0eQkNDwdnZGY4dOwYAcpmt0+lg2rRpMGzYMAAAKFWqFLNd+fyICDqdDtq0aQMVKlRgdggvs+7cuSOy25XGTiE9PR2Sk5MhJydH1hYPSvNhbW0NISEh4ObmJsJNeno6/Pzzz5Ceni6ry8LCAiwtLUGn0zGZm5eXJ6vfzc0N4uPj4caNG7Bz507w8fGB3r17y/rAj//y5ctw6NAhZhvUrl0bHj58COHh4XD37l24ePEiDBs2DKZNmwZbt26FTp06QV5enuZ4eR09dOhQRg916tSB3377DXJycqBFixawY8cOVd3x5MkTEW/6+PjAuXPnICoqCpYtWyZrW6o3qY129OhRRdtQSz5TPygyMhLGjRsHgiAAIQSSkpIgIiJClv/8+fPQrFkzSE1NFf2u0+lg/vz58OGHH0JWVhbUrFkTLl++bNT/EwQBmjVrBpmZmXD8+HFVf00LdDodfPbZZ2Bpacnau3HjBqxZswby8vJk+evWrQsNGjSArKws+O677yArK0sVR4QQGDJkCISHh0OvXr3Yb+PGjRPpER4vvPwzl2+VYMuWLfDw4UMAAAgLCxOlmaKrlPQNQNF9PD6tqH6zUp2FhYVw/PhxyM/Ph6CgIPD19VUspwZSfYuIkJGRIfIhTAUpP9D6+/fvDz/99JNmWWm8Bg0b2dnfsbGxsHnzZnBzc1OkFwCDLpg/fz5Uq1YN+vfvz+IKakBlGV9HnTp1YPbs2RAVFQUTJkyAs2fPwrZt28DT0xM++ugjWR2ICL/++ivjPyW/0BTgx0/HTgiBpk2bMvkHAPDLL79Au3btmHyXtkNtdq22CSEwY8YMGDx4sGJ69+7dYdWqVdC5c2f48ccfRTa9g4MDeHp6MjvS1dUVSpUqBe+++67RMVL9mJWVBRcvXpTJF0EQIC4uDg4cOAAABn/zxYsXij4YtXfT09NltsOBAwfg0KFD7O9atWoxWggICFDlSwopKSng7OzM7OOKFSuCq6uryBdQiikdPHiQjTEqKgpu3LgBz58/Z+lq9ufrxi8olCtXDlJSUpjtpkSLjRs3htzcXIaznJwcWLRoUbHEHXle0uv1UL9+fahdu/Zr+wnPnz8XyX0ePD09ISwsDF68eAHnz5/XjGv5+PiAXq836juq9YXGKKQ6AMBAVwUFBZCUlAQABh+qTJkyAGCwb5cuXQrPnz+X8Yq3tzd8+OGHEBUVxezKP//8E44ePQoAAD179gQAgx3XtWtX2LRpEwAY/G3e77CxsYGcnByoW7cu1K9fHy5cuACbN29W9CciIyPh0qVLAGCYp4KCAtlcUxoYNGgQfP311wAAzPam7Tdq1AiWLFkCy5cvB0EQICkpCcLCwsDf3x8ePHgAkydPhpEjR7L+T5o0Cb788ksR3/O0Vtxx7jJlysCVK1cgICAAnj59Ci1atAB3d3dYtmwZEEKge/fu8Msvv4hiJlKQ0m5ubi5cvHhRxNeUl14ntqrT6cDCwgJycnIUdSGAwWdSijncuXMHdu7cCQBy2wbgb7rWom8KNjY2EBMTw/IHBARATk4OnDx5EgAAAgMDIT09HZ49ewbVq1eH5s2by+pQ4/cnT57AnTt3wNnZGRISEoz2RQpKcScRGF2eK4F/BJR29Ch9L1++1KyDrrxu2LDhtfrz6tUrTEhIwISEBPYmkBTy8vJw7dq1+O6776Kfn5/ZK9uCYLg+KT4+Hjdv3qw6dv44rpeXl+oKNiGGe4zr1auHc+bMwefPn4v6/ezZM9y3bx/u27dP8QoJU2D58uUYEBCAAQEBeP36dZPKHD58GCMjIzX7/dVXXymWzcrKwjNnzuCZM2cwKytLlj5u3Lgirbq/Cbh58yb2798fq1SpgpGRkdihQwfcu3evKM+VK1dk73wQYrgS6Mcff1Ss98mTJzh9+nSsVasWO31mjNa0djiMHDkSf//9d/Z+A3+U+I8//sAWLVqw3/gvLCwM169fL+sfLXvhwgVMS0vDtLQ0zM/Pf6P4PHr0KLZs2ZLt2paO38fHB0ePHs3uMec/Ozs7nDRpkma7w4cPZ2+oqX1aPMbD9evXFXGNiFhQUIDff/89RkREmCQ/aHpERAR+88032KFDB9FuY0EQsHLlyuxtKGl7U6dORRcXF1H++Ph4fPToEevvrFmzRHXyX8OGDfHp06evPbeIiNu2bUN/f38MCAjAw4cPs/8nJydrlpPiTIv2+d/LlCmDvXr1wqVLl+Ly5ctxxIgR7KNXThJCRA91L1++nN2JLggC/vzzzyKZPGvWLFb/xIkTi0VXadGLEty+fRtTUlLw9OnTRSqn1R7VS0pvsP6X4NChQ3jw4EGjNKUEWrIwJCQEe/Toge3bt8eWLVvikCFD8OrVq6xsXl4eXrlyBVu3bi3jz1q1auHBgwcV29Sa+0OHDmFoaKgs7cmTJ5iYmMj+pu+E/Vt6UAnGjx+P48ePx8ePH5tcRvqItdqXl5cnK0vnfeHChQz327dvf+1xqNkwhBB0dXVl19QqgTEbRgnelE0hhdu3b+Po0aNx7NixWFBQYFIZKWzYsIHR3bFjx0RpajRK5a0aHyEi/vXXX9ixY0ejfGSq/tACLZte+ul0OrSzs8M5c+Yo0iCFrKwsHDNmDHsrypQPwPDGVO3atRmtXL16Fbt3744JCQmKc0TxQmX2nDlzMC4uDuPi4lie/fv3s3w8HRUnXderVw/j4uJU34R4HZlMgbfRtOxeAMP1R2PGjJHZaFJ8KUFGRoZZ9EmI4baSunXrKpa7fv069uzZE21sbEzyEUuVKoVt2rSRXRnO46Ndu3Ysf40aNTTlBF+3l5cXu3bQVP2RkZGBnTp1YnW89957ZvPtmwBT5rao8G/5zaaAGj9IP51Oh6GhoRgfH48PHz5Ure+7774z2a/kv4iICFy6dKlMPpnCR7t27cKwsDDNdkuVKoV169bFzz//HE+fPi3rt6l67Pr169i3b19F/1ALf0pj9vb2xo8//lh1PtV0OJWTcXFxWL16dfbWGf9ZWVmJrhWXQkZGBjsJOHv2bNH4zPEFtNpRm78xY8Zg7969ccCAAWbzu5KuKgoUFhair68v6vV62buAWsDLiYyMDPamc3HYzmrzXqdOHQwODsZWrVqZZbvl5uZikyZNNHWeqXzr7++PgwcPxjt37rz2eHmg8RlXV1e0t7fH6OhoXLx4sYgnDx8+bFZ8xlQwZoeYA2/STi4qzvj5tbW1NRlnPM0vXryY4f38+fOyvFqxMDU605KfVlZW+P7776vGuFNTU0W2JM/XvH1alPi+Fl+rxVbV4vS1atXC6dOn45MnT4rF79KC5cuXm/Q9e/ZMVpbGhGicicau3iYoOfn2PwT8DrAyZcqAhYXFP9r+ixcv4N69e5CVlQU5OTlgbW0NDg4O7PSEUpqPjw+UK1fOrL6mpaXBlStX4MmTJ5CamgqFhYVgZ2cHlSpVgoiICNDr9W9glK8PZ8+ehTNnzkB6ejrY29tDeHg47N+/H9LS0qBUqVLwxRdfmFXv3LlzYePGjQAAbDfW2woXL16Eq1evgl6vh8DAQKhcubLirmMp5OXlwfXr11VpydXVFZKTk+HJkyfw4sULlubr6wshISEymli6dClMnz4dXr16BQAAt27dAgADbZ09e1Y0RyEhIYp9oruT+/XrB6VKlXodtJgNavjMzc2F3bt3i9IaNmwITk5OJtXL85gp+NQCNVxTSE5OhqNHj8KVK1dU5zciIgJiY2NFc5GRkQF//PEH2yXm5uam2V5+fj5cvXoVcnNzISQkRHT6j0JSUhIsW7ZMhLdWrVrBO++8Y/J4/wkwF2dK9Tx48AAADLt27927BwCGnW73799nu5Cio6PB3t6elTNX55hSzhi9qEFxlMvPz4ejR4+Ct7f3W6tH/ikoiixUAjX+1AK1OczNzYXJkyfD9OnTITc3FwghUFBQAACGHWx9+vSB5ORkAABRWgkULyjZMLGxsWBjY/Ov9clcm6K4ID09ne0w9fPzU2z7dWjUHD56XVCz6b29vUV6wBTIz8+HAwcOmKyv4uLiiuQXmGKHSfUcPZlN4W2ka2Ngro12+/ZtAIBi03Hm0ueFCxfg119/hbS0NLCysgKdTiejh8DAQJPaN8UWUZMTb0p//Bt8+38ZisNnuX37NiQnJ8OPP/4Iq1atYqcfunbtaraNbSpQeiksLAQ3NzewtbVlbVlZWb12/VK4deuWSB4/efIECCFgZ2cH7u7uQAiB/Px81XGbwpumQlF1+MGDB2HFihUAADBixAgIDQ0VpZvrC7wO/Jf4XaoDcnNz2Ylbf3//f6wfr2O7qfG7p6cnvHz5Eq5fv64oC8qXLw9+fn5veGTa8Lrxmf8imGIna8G/hTMtvRIQEAA3b94U2bT379+H+/fvAyEEAgICIDIy8rVsydeJ75vC18Ziq2px+n/b79KC27dvq8au/m0oWXwrgRIogRIogRIogRIogf805ObmwsaNG1mwhV6DUgIl8LZACY2WQAm8nVDCmyVQAiVQAiVQAiVQAiXwpqBk8a0ESqAESqAESqAESqAESqAESqAESqAESqAESqAESqAESqAESqAESqCY4P/2fUolUAIl8H8e6HFyAMMD7SXw34H8/Hx48uQJ6PV6cHV1/be7wyAvLw+eP38O2dnZYGNjA/b29v/4NcAl8M9CdnY2PH78GADgX7/OpAT+u3Dp0iX47bff4PHjx6DX68Hb2xtiY2PB29v73+5aCfyPQUpKChw+fBgAAHr06PFG23oTurqEV94OeFN2WIlO/efgdf2gEpu3BEyFpKQkuHbtGruqLCoq6t/uUrGAlhws0VXqUBKD+W/A/yrflsD/BvyX7MWSk2//QcjPz4czZ87A/fv3AcBw/2vVqlX/EUO3sLAQbty4AXfu3IGsrCzIzs4GBwcHcHFxgYiICJHB8fDhQ1i/fj2cOHECHj16BK9evQI7OztARLCwsIBnz57Bq1ev4P79+5Cbmwu5ubkiw93X1xfCw8Ohdu3a0Lp1a3B3dy/28SAiXLt2DXJycqBcuXJgZ2cny7Nv3z5Yv3493L59G+zt7aFChQrQrVs3s+53T0tLg1OnTkFubi6EhoZCRESELM+OHTtg6dKlIiXXqlUr6NmzJ1y7du0/acAhIqxfvx7WrFkj63+dOnXgo48+gipVqhS5XmP4fPnyJaxZswbWrl0Ld+7ckeHz/v378NVXX8H8+fOBEAKtWrWC7OxssLe3h1evXgEiQl5eHmRnZ8vovXz58lC9enVo1aqV4rthSvAmDPCMjAzYvXu3iD7r1Klj9r3LL1++hLS0NAAoPgVmqswyRb48f/4cFi5cCGvWrIELFy5AYWEhAABYW1tD7dq1oW/fvtCuXTuT+lUUeaYFZ8+ehU2bNsGxY8fg6tWrzADgoXTp0kyetWvXDqKjozXrTE1Nha1bt7I67969Kwtu+Pr6QlhYGAQHB0NoaCgIggB//vknWFpaQkREBFStWpWNobCwEK5evQqXL1+GI0eOwM2bNxk/tGzZEho1agRJSUmwfPlykexp2bIlxMXFmaVztOa9oKAADhw4YHR8FGf169eXvdEzceJECA0NhbZt2xp9D+NN0DUPa9asgXfffZe9kfFfgNeVy1o0am1tDVZWVuDu7g7Vq1cX6fHimHspvM3BjZcvX8KNGzcAACAyMhIEQZDl2bZtG4wcORKuXr2qWEfTpk1h0qRJqvNx/fp12Lhxo0gPtG7dGhwcHIod10rwpnT8fwnehIx5+PAhHDp0SCRD69atWyx0vWbNGujWrRsIgsBklql6xxTb3BxdXRy80rJlS5g5cyYEBQUppmvJitu3b8ORI0fgypUrinYBtftq1arFbKyMjAzYunUrHD58WFbOzs4OLC0twdraGhCR+TnUzrS2tgZHR0ewtLQECwsLyM/PN5s309LS4Pjx43DlyhWwtrYGX19fkc5NTU2FyZMnw5YtW+Cvv/6CgoICIIQAIoKdnV2xz23t2rVfi5Z4nZqXl6cpX7p06QI5OTlm+VadOnWCpKQkuH79uiJNqMlWS0tLePXqFbi4uCjiSK2cs7Oz6hz+W6AkC7TgTdi8psIPP/wAoaGhEBMTU6Ryhw8fhpCQEPDy8ipSueLyE7Tg2rVrMGfOHDh79iw8evQIsrKyIC8vD3JycsDBwQHs7e3Bzc0NHBwcGP0Yk0vmjL2wsBDOnTsHqampEBgYCOHh4bI8Sn5C1apV4fTp03DixAnGg3379oX69eszP/3mzZvw3XffsYUWCt7e3tC1a1f4888/TY57REREwJ07d8yOpRTHO5cvX76Eixcvwtq1a+HQoUNw8eJFmRysWrUqbN261WxdVVQ4d+6caA4aN24sGisiwoIFC2R4admyJTRv3hxsbGwgJCRE8X0kNf+wUaNGRe4nH3u7ePEiJCYmymSPVuyN6undu3dDdnY205um8sO/Cb/99puqHktMTCyyfDIF1GyUly9fMpvCysoK/Pz8YPjw4dC9e3dW9urVq9CzZ084c+YMAADDp7e3N3z55ZfQrVu3Yu+vGvz1118iW/h1cJWWlgZ79uyBW7dugZ+fn+IGNDUZ0qJFCzh79qyi/Hn58iU8evQI5s+fDzt37oRbt24BIoKVlRXY2NiAu7s7ey/udf0uUyA1NRWuXbsGAEVb3FaK30RHR8OWLVveSj+P2jAABt34Jt5ILTbAEvjPwLNnz3DIkCHo4OCAgiCIPnt7exw4cCA+ffq02Nt98eIFzp8/H+vXr4/W1taytvkvODgYP/nkE6xcuTICgNkfIURUr16vx7p162KTJk0wMjISK1WqhF27dsVNmzaZNIZLly4hIQR1Oh37bdGiRejh4SFqo0+fPpiVlcXy9OrVS3GclpaWOGXKFJNxmJubi3379kW9Xi+qp169epiUlIQXLlzACxcu4MCBAxXbI4SglZWVKt5bt26NN27cMLk/xQknTpzAdu3aYefOnTEvL0+WfvfuXYyJiRGNhX78GPr374/5+fkmtamFTx4PTZo0YTQlxaeNjQ37P093/N9SmpT2WRAEtLW1xY4dO+KhQ4ewoKBAsb9bt27F8uXLF2n+Ll68iB06dEB3d3d0dHTE6tWr46pVq1j6kSNHsHHjxqjT6WR8ExwcjPv371fF3/Xr1zEwMBDLli0r+v3HH38U8e7r8pgpMuv+/ftMvlhYWKjKAEEQ0NfXF52dnVXniOZr1aoVPn/+XLGfv/32G8tvijwbPXo0pqSkKNZ14cIFrF+/voy21D4+X4MGDfDixYuyOu/fv4+JiYky2ubrKYosDQwMxAYNGijije9TaGioYps0n7Scls6h825nZyebGzs7O6xRowa6uLioyjql311dXXHixImieaV5nZ2d8cMPP8QjR44ozlNx07USrF69mvXnbYA3KZdNoVGeZmi6TqfDypUro7Ozs0lzT+txdHTE8ePHK/I0la1K7f3bupHCwoULURAE9PPzU0z/8ssvjeKQfi4uLvjFF1+I5nTSpEmKc2FtbY329vavzWf/ho6n8OOPP2KbNm2Kze4rbggMDMRmzZrh6tWrcfHixYzOXxfu3r2LHTp0UNVNbdu2VdVLpgIvs0zlaZqfp82EhAS8f/++qG5vb2+0sbExqgulutoUXjGmAwkh6OzsjKdOnRKV3bp1K4aEhKjahGpySenz9/fHMWPG4McffyyzzYuqo435P1q8iWiwh6mvolSXra0tRkREKKYrtWtsbi9evIhly5bVpBGlcRijJUKIqD1Kn4QQVfkitd9pHlN8K6V5ktJnWFiYIk/odDqWz93d3SSZLAgCuri44I8//lh0Zn3DYKr98iZs3qICrTcsLAynTp2Kd+7cUcx35MgR/PTTT7Fly5bYpk0bBADU6/XYpEkT/PnnnzE7O1tznG5ubmhtbf3afoIWXLx4EYOCgsySE2py6YsvvsBXr17JcGZs7F9++SW6urqK+KJHjx6YmprK8nzzzTeqfoKSTK1atSrLX1S5pxT3kPIsz++8nTdo0CDV+YqMjFSlGR5++eUXrFy5MtauXVvR7hk9erSoH2pykJcrSvyhpKuKCpmZmdisWTPZWENCQvD06dMsX8eOHRX5lh+HTqczad7p17hxY8zIyDC5r9LYG41hEEJYHrXYm16vR29vb/a3luzW4oc3AZcuXcJBgwbhkCFDFOklMzNT06YTBAGtra1xxowZxdan9PR0HDBgQJFtlKCgILx+/Trev3+f4VtNBs2cObPY+qsE+fn5OHfuXCxXrpwifc+aNQtzc3NNro+PH/J4MEWGqMk6Wvb58+fYrl27IstuNduuOGD58uUm+UHUz2vfvj0OGjRIFrfj7TWp/ODzmePnFQesXr2a4ddYTOjfhpLFt7cEAgICsGzZspicnKyYfv/+fRa811LiERER+ODBg2Lr17p169DLy0tT0WkFisz97Ozs0MbGxiShVb16dfzjjz80x0GDMNSxmDZtmqIBIggCNm/eHAsLC/GLL74w6lTMmjXLJDxSg0epjtKlS6MgCOjm5iZK8/T0RDc3N1Xn8E0YcObAsGHDkBCCjRs3lqU9e/YMw8LCRGP38fHBmjVrYvXq1bFUqVKiMXzwwQcmtamGT0IIenh44MKFC7FXr14ivDk5OYkWA8z91GheEAT89ddfZX3VCqyqzd+ePXuYwydVbiNGjMD4+Hijzoy1tbVifxDl/JCRkYF169ZVNS6UeEwqs6R1miKzqENoinwxNh9SGdGqVSvZuNetW4elS5c26hBJcW5lZYUTJkwQGdTbt29ngW2a387ODitVqoQtW7bELl26YI8ePbBLly7YsmVLrFSpEtrb24vqtrOzw507d7I6T58+jZ6enjKcOTk5YWRkJMbExGB4eLhokbK4v6LIckKITOdI553Pq8VL9vb26OHhgX5+fujh4SHCFY+z8PBwvHnzJiL+HYTh5yooKAgnTJiAN2/eNIuuEREPHTrEvuXLl7O+8r9Lv7Fjx7J+HD58WJb+T8Obksum0GjdunWxbNmyrz33Up7m5x5RLluV6v43dSOFjh07IiEEBw0aJEs7ePAg679Op8NOnTrhkiVLcM+ePThkyBBFOS8IAiYmJiIi4vfff29UVr4un/0bOp7nXaXPHLvvTQAve6jNSggxWkan0+Hly5cV069evcrsbi3d5OXlhdeuXUNExAkTJhT569Spk4gupG1IeTomJgYjIyPRyclJRo+enp7422+/ISLio0ePZDTo4eGBDRs2xM6dO2N0dLRo8YLX1Vq8sm/fPkXa5vnfysqK9d/LywufPXuGiGJZoSWXlOSHEv7NsVWKooOdnJxMloMtWrQoFt2v0+lkNrLS3PK0aW1tjbGxsditWzfs3LkzhoWFKY7Hzs4OAwMDMTw8HCtWrIjh4eEYGBgoa48Qgq6urrho0SIcO3asqB8Uv6VLl8by5ctj9erV0dLSUpUmtHwre3t7o3Ngil3E04mWTOY/nU6Hq1evNlXEFBlWrFhR5O/jjz9m41BKR3wzNq85ILXPdTodNmjQAFesWIHPnz/H3Nxc5iPxn5SuHR0dsVevXnjw4EFR/cXlJxiT8zt27BBtnjRXVqjp76SkJFWcScf+6aefqgaYq1WrhtnZ2bh161aZPFTqU+nSpWX2mLTPDg4OSAiR8W/p0qU14x5qOKCB+KdPn+LSpUtl/C6VAdHR0UYDxAMGDEBCCLZr106W9ujRI7S2tmZ9kMrBcuXKifro5eXF7LqdO3fi3LlzsX79+oq6qqhQWFgo8nOkn4eHB96/fx+nT5/OfnNwcMAaNWqgu7u7Kj615l1Kb++88w4iGpc9nTt31tSXy5cvF9klxuSuKbyhxA9vAqgPGBsbK0t79eoV1q5dW4RHCwsL9PLyQnd3d1msZ9y4ca/dn6tXr2JgYKDi3JkiWywtLbFmzZpICBHZttLyer0eL1y4YFYf69evjw0aNFDduJCZmSna7KEme+vVq4eZmZkmtSmNH/KyzpgM0aIxf39/LFeuXJFtQCmd8rZdcQBdfBMEQXOtgfp5dnZ2Jsl5Ly+v1/bz1EAr1qIVg5HKMEEQ0NvbGxMTE4sdr68DJYtvbwlQQlEz0mJjYxlhu7m5Yb9+/XD+/Pm4YMEC7N+/PyN8QTDsLisOmDdvHnOQeeHLE7e1tTX269cP+/btiw0aNJAxp7OzM3bq1AmbNGkiM349PT1x7ty5ikxNmcbOzg5dXFwUjWapQb927VrVsfBBmD/++AMtLS1REAy7DT766CMcNmwYE5qCIOC6devQ1dUV3dzccOHChfjgwQPMy8vDlJQUnD59OjMc7ezs8K+//tLE465du1i9Li4uOHjwYJw/fz4OHjwYXV1dGS7pjrMePXrgvXv3EPHvwBw//g8++OCNGHDmAt2ROn/+fFnaqFGj2NibN2+uSN8HDx7EypUrs3xSR0gKWvhUM/Yp3Zjj3NStW1dE+/b29njw4EE8evQozp49W6SQbW1tRf3XCqyqzV9KSgpzXgghGBYWhtWqVUMLCws2LiVnRklJ+vv7K+5wlAYlmzZtqliHFo9JZZa0TmMySxp0sbGxkZ184+WLj4+PonHo5OQk6id1ZgVBwC1btrD+8vKMlvf29ma8LG2vUaNGMoOiZcuWmJubi7dv32btWlhY4EcffYQnT5406szl5+fjyZMnsU+fPmw+nZ2d8c6dO5ieno5lypRh7TVt2hRXr16NDx8+VBwDlclKBqGFhQX6+/sr8oG1tTXWrVuX/d2pUycRfX/55ZeYk5MjCj4YMxx5ncPPu7OzM8sza9Ys9jf9goKC8MSJE6ontp8+fYonTpzAadOmYUREBKs3PDwcs7Oz2bxIaYB+Li4uRaZrnralQRu1hQCpYyD9/U2evFGDNyGXTaFRCrzBf/PmTSxTpoxo7h0dHXHPnj2ac09lI9WN/NxLZWu1atXY3L4tupEC3bG5efNmWdo777yDhBB0cXHBo0ePst/v3bvHApeWlpZYsWJFkfwXBAH37duHZcqUQWtraxw+fDgeP34ck5KS0NfXV0Tvtra2uHfvXrP57J/W8YhynVS6dGkZnxfF7ntTQOuXypiyZcvi+PHj8c8//1Qto4SrvLw8dpKT6v+vvvoKd+zYgTt37sQZM2aI5igqKgoLCwsV5Y4pn1YwQAsqVKiAhBCMjIxk+b29vfHJkyfYv39/WX20PYqXgwcPsjnmdbUWr3h5eSnaPDze7ezscMyYMWxTz9SpU2Wygpbfs2cPRkdHy/Skvb09fvjhh4znrK2tccGCBXjq1ClcuXIl9u/fH/V6PctvZWWFX3zxBe7duxd/+uknkZ6rXLkyrly5EletWoWDBw+WBZvj4+Nxy5Yt+NNPP2H37t2ZXaDX63HdunX49OlTNsagoCAZb+7atUtRvzdo0ABtbW1lc+vk5ITnzp0T0a10rqgdFhISoji3tGyfPn0wPT2dzQ+vH/j6eF1gKg2q2ey8ncf7Avy8du7cWdO3unfvnuLmTrpwKwiG3dv8XLm5ueGePXtk5SwtLZmdoSaTk5OT8eDBg/jxxx+z3fZubm5FOi1SFDBXFmjZL2/C5n3d8fG2MO2rvb29iE/UfCOpXAoMDMSxY8fiuHHjis1P0JLzmZmZIvpydHTEbt264eTJk/Hzzz+X2cqurq44YMAAbNGihSiNyriVK1fioEGDmN1PCMFSpUqxBQepnuLHTuWqIBhu9YmOjhbxhCAIOGfOHKxWrRqz948dO4Y5OTk4evRoGb9v3boVN2zYwH7T6/Wo1+vRzs4Oe/bsqcib9Bs6dKhq3GPy5MksHy9/aZxIEAScMmUKhoeHi/idQlJSErMJBUHAn3/+WZPOqlSpgoIg4Pfffy9L43VckyZNRHIQ8W+7jspSqT9K4ccffxTpKnNgzZo1bEyBgYE4Z84c3LFjB86ZM4edUP7ss88YbYwbNw5zcnLw+PHjIjrgdZaxec/KysJt27YxW0UQBNy6daum7FHiQSWelPJnXl4e1q1bV5Sm1+tx4MCBqjxNiOHGDLpRSMoPbwLq1KmDgiDg119/LUubMWMG61tUVBTu2LEDc3JyWPqzZ89w+fLlzHbX6XT4+++/m92XFy9eMP2t1+uxe/fuuHLlSty7dy+uX78ea9SowXBWp04dzMzMxHv37uGqVaswPDxchFMHBwe8fPmyotyleSwsLLBHjx64b9++IvVTS0YiIrZr1461ZWlpiS1atMBhw4bhZ599hi1bthTp7M6dOxttTxo/5G/HcnV11ZQhtCyPm8TERBw8eDCLU0vTt2zZIrNReFq1tbXFKlWqoJ+fn8y2Ky7g7S8tfMfExIj6xsftmjZtKuu7dK3BHD9PDYrTn+F1ZZ06dfD777//12IBFEoW394S0GKIzZs3s/SmTZsqru5nZmZi48aNWb5ffvnltfpz+fJlJtQqVqyI+/btw/z8fFb/unXrmONcvXp1fPXqFX788cciY4gS+wcffMB2hPDHfAkhuGHDBlbnsGHDZMF3nnFoWWo8nD9/nu2Spcpq4cKFiuPhgzBDhgxBQgzBrlu3brE8L1++xMqVK6MgGK4RsbOzU1XUhw4dQp1Oh4Ig4PTp0zVx2aVLFySEoLu7u8zhuHv3rshpbNu2rSidD8zRHSzSPIjFY8CZC2XKlEFBMJz2kIKfnx8KgmFnc2FhoWodz58/Z9fidOvWTbM9LXyaYtwV5aNBu4KCAhw2bBj7PTg4mDmdNFhEHdOAgAB8+fIlIqoHVqXAzx/lKwsLC1EA6tq1a8xpod+QIUPwxYsX+OTJE9y4cSPGxMSIjCJCCNu1ygPPDzt37mT/r1OnDk6ZMoX9rcVjUpnF12lMZlH5wvP6rl272FUUy5Ytk8kXfre9o6OjSL5I+ynlJ16e0R2JhPwdXDx79qysPTrve/fuZQtKtD0qQxwdHfH48eOa9KoGx48fZ8f6hw4dymSkpaWlYkBZTSYnJiYiIYaNAPxuy9GjR2Pr1q0VnZMPPviA4alUqVIoCAKru2vXrvjFF1+w/La2tiy40L59e9Ugxi+//CKb95MnT7K/v/rqK/Z/2s+i6KrCwkK2c1MQBJEzc/HiRdy1axd269YNbW1tZX0UBAErVqzI+mpMd0gDFfw4zfmoHPkn4U3IZWM0ygNv8PNz/9577zFZZ2x3J5WtmzZtks29VLby7fHwb+pGCk5OTigIAp49e1b0e05ODgtyLFq0SJRGcW1ra8uuDaIBH1qmbNmyaGFhIbpimMd19+7dGf0vWLDApL4q8dk/reOlOunSpUsszVy7700BrT8+Pl60UYx3CGNjY3Hx4sVMD2rZ+z/88ANLT0xMVAxu5+fnY8+ePVk+3o4uqmxSkuWm4Izq6uPHj+PatWvRwsKC8TTdTU8Iwfj4ePT29hbVS7/atWtj2bJlRbpajVf279/P+ujh4YHbt28X2T78KQRbW1ts0aIFEmK4rpCXFTx++av2xo8fjytWrJDJip9//hktLS3R3t6enbSkusfCwkKkk/Pz81matbU1C+bs2rULEcW8SXHv6OgoCtxevnyZBc1KlSqF6enprMylS5dkvBkbG8vGLd39rRScpDqX1rF27VocMmSIKJjN00JoaCgLttG5FQQB33//fRlN8Pph9erVWLNmTeazaNGntE0tm5ynI94XuHbtmki+aPlW/A5pBwcH5ls1a9YMg4ODRbJ15MiRjCYaNGjAZDJvJ3/55ZeycmrXvvOBclNlclHBXFmgZb+8CZv3dcd35MgRXLBgATudIaUdvV6PnTt3xnPnzuGTJ08UaY0fI5/u4eHx2n6Clpzv1q0ba6tJkyYiOc/LJcqXhBjiJRSoHAAwXEVNZUhBQQEuWrSI2cFULvH8/tlnn6GPj48izipWrIgJCQlMVpw7dw4dHR3Zade6deuKnleoVasWCoJh442/vz8KgoBdu3ZlvEn9PEEQmJ5W4s2+ffsiIQSrVaumGveQ+v68bTd8+HD08PBAR0dH1TgJosHmojdzqOWhwOs4KfA6TsuuW7hwoUgOKgHd0BATE6PZHzVo06YNEkIwMDBQFlR+9uwZli1blvWnV69eLI36jg4ODnjmzBnWz6ZNmxqddwpPnjwRzbupcp7nPy1ZL9XTiYmJzH7gdeEnn3wia5fmo/8HACxfvvwbuxLP19cXBUFQlP2hoaEoCALWqlVLc2Hl4cOHrJ7evXub3Rdqh9jY2CguiFG+pXRBbRQKjRo1YvNgZ2cnskN4uSu1LQTB8DzI559/jlevXjXaTy0ZeeDAAZZepUoVxZNLN2/eFC34nDx5UrM9qQzh/ca7d+9qyhC+LPVNaZ5Ro0apxg95vM2ZM0e0gYenU36zcnFePWrq4hu/KUIat6N+XrNmzZh8VorfFMXP04Li8meU5IkgGJ4L6tatG+7atUvTb31TULL49paAFkNQhvf399e8D/b58+dMCdLrL8wFegVFuXLlRG+g8f0sLCxkJ9rGjh3LjFVHR0fMz89ndeh0OuaYjBgxAidMmMCYoVu3bqI6f/31V+botW7dWnQ6hmcuGxsbnD9/Pqanp+P69evZSTi1ABsfhImOjmbOhBRWrVolUuZaQA2Npk2bauajxqPSbhhEZEEDQojIyJMG5hYtWsToQAle14AzF2gfpcfOHz9+zHB57tw5o/WsWLECCSHo5+eH9evXV/0ovoKCgmRpUmFLTwVRJ4Q6TcuXL2cOBn/9TP369dkCQ3R0NC5fvlzUxwEDBrC8dFGLBotWr17NroqcP3++ZmBVCej8Uee0S5cusjz0dAcAyK5VpDiguz754IIUT9WrV2fp1ImwtbXFunXrsh0/fPBNicekMovnMWMyi8qGkJAQZmgmJiYyfP3+++8i+cKfagwKCsLs7GyRfKHBab6fdOx8e+XKlcPTp0/LxoeIMnmm1medTocBAQEoCAJ+8cUXRudVCyge6VVMVEYqgZpMpkb9+PHjsbCwEAMDA1nwIDQ0lOFCEAzvX9Ex8PJUEAT86KOPkBDD7npah06nwzt37uDMmTNZGsXd0KFDRZsl6tatK5t3niaqVauGgmDYLV8UXSWlXXqCysHBgbVdvXp1ll6nTh0MDQ3VvJpz2LBhePHiRVXdQf/29PTEDz/8kMmQ8ePHq350QYDOhfT7p8FcuRwYGCj66AlInU7Hdmo7OTnJ8imVU5p7RMThw4cjIQQrVaqEiYmJqp+lpSUSQrBVq1aYmJjIFm3d3NwYDdeqVQsTExOxTp06inyN+O/pRgr0Gg/+7Q1ExJSUFNbntLQ0URp1jvnrO6gzSgNrSkFwKa6bN2/O7KmiwIcffsj05ZvQ8VpAbSutHaBFtfu0QMveMPbxMoi/cs/Ozk4WBLKxsWEyUs3ep4E0Y0EiekKOOrm8zPr+++/x4MGDRj9+EeLdd9+VpWsBr6sR/+ZpeiqP6pXLly9jQUEB7tmzB+Pj42V4oXmtra1xx44dqrzSqVMn1lelK3Po5ie6SY8GrZ2dnUV2GE8TlDd4W0tJVkyaNAkJ+fv6sRo1aqAgGE4TUJ9FEAzX9fFpdKGvRo0aiCjmzW+++YaNR7qB786dO8yu5AOMlF543qQ+kouLi8zW4k/LUtqgOlda57fffsvoVhpA4u0pWk4psCa1YdatW4eEEHYajvZh1KhRIt1IZflHH32E48ePx9q1a8t0fJ8+fWQ6VepbSeWLmm/F7/qX+lbfffedTLZSmqBXLlKZzMtWpXJqYK5MNhV4XH/zzTe4fPlyox9d/BAEQTGdXi1cnDbv646Pl5/Jyck4evRokT/Hy156ekdqu+p0OoyNjZVdQ8r7UNu3b2cLD0XxE7TkPN2o4ebmJpPzvFxatmyZKF7Cw9ChQ0UyhNdLUVFRLI0/yUJt5bi4OKxUqRKL26h927dvx3HjxrGxSAP5dIPBxo0bRX4C5c2EhARWln87Usqbe/bsQUKITF7zvKkUS+Hl9ddff83a0logNhZLoSDVcRT++usvkVw1ZtfxclAJqF3n4uKi2R81oD70t99+q5j+7bffsv7wY+F9R0TDRjW+n+PHj1eddx6U/EMl2RMQEMBiZlLZw/PdO++8I0qX6mk+9sbz2KRJkxAAsHbt2vjee+/JrrylbVSrVk3E08UFNP4jtZPpwj9dgDEG8+fPZ/g0F3g7RAko337yySciG4XCxo0bGY0TQhTtEETEpUuXiuwEXuZSuUtjtUqgJSPpBrPSpUuL3h9EFPupdGGI2gxafim1C11dXWV+av369TE4OJjRSZUqVVRjj6GhoQhg2AhZv359ka2itvhGx7ho0SKRH61kD9vZ2aGbmxuTe6/z8WPUwjfFoZeXl8iWlPp5xuI3pvp5WqBlL2rFYCjuP/74Y/b7yJEjsXXr1livXj1FOvXy8mIxoX8KShbf3hLQYghqcEybNs1oPfSdsoiIiNfqT0hICAqCgEuWLNHs54kTJ5AQwpw6AMCPPvoIEQ07sOhJMnrN49GjR/HOnTtMwAQHB8vqfO+995AQgi1atGBjj42NZc41L9isrKywXbt2OGfOHCbElALYvMNNryfYtm2bbNy0b4IgGL2X/ueff0ZCCPr6+mrms7W1RUFQP4LLG6H0xBSi3ICjiwY2NjaK9byuAWcu0LmX7vZJTk5m/TflMdSzZ88iIYYdw1IFzn9KjhX9ePqQ5qO79+m8UnzyQfp9+/YxGrC0tJQ90vvXX3+xvHTRlQ8WUceL3mGtFlhVAjp/tMy6detkeeiiCCEE16xZI0qT4kMJB2rp0jxKAcsrV66IeIwPriGKecyYzOLlCy+z6MO+dDcNlS+lSpVi/aQOBi9f+vbtK+qnt7c3yz9ixAhRe1oBWV6eSYFvj+62NLbLyhjQk2H29vZst5XaCUk1mUwDQtQ5okE0PlhE5yozM5ONgf5G//3111+REMMJOsoTLVu2FOGFBk7pvB88eFC0i8vNzU007zyuqbF/4MABRDRdVxWVdtXySI1iQRCwcuXKOGLECHblA9Udn332GTvJERQUJDKk1YDfnfk2gLlymTfS6aeEQz5d6ePliHTujx49ioQQ0eKJqbK+qHIL8d/TjRTo5qNVq1aJfr97967qXFBe4k8/Ux3JX7UivZ5Piutly5YhIYZd0UUBijNXV9c3ouO1gOqPxYsXa+aT6iQtu08LtGjQ2CelQ/pvVlYWLlu2jL1XoRQESkhIkAVqfHx8UBAM1z0Zg9mzZyMhhk0sO3fuZMEtGxsbnDx5stE54R8o1zqZrwRSXU15mn+nTMmnUcILL5dp0FpqO9A3JAkhiuOidti3337LrjID+PvGDGqH8TQhtQkRlWWF1Cakp8XpW568z8Kn8bvFnz17JuLN9PR0Nm6lTQGfffYZEmLYXCD1kXjepOP88MMPZXVQm4LiduLEiUznSuvk/Qu6WBYVFSW7go9+AwcOxPPnzyu2R2mJXm9pZWUl0qkRERGiU7RS+UJpib8dQWnOpb6VVL6o+Vb8FVFS3+rWrVsy2Sq1zenvvGxVKqcG5spkU2HBggXMX/bw8MCVK1caLWPMfqH8VZw2r7mgFS+hJxJiYmJk1xRTWmrYsCFOmjSJ8Y9Op8NZs2aJTrtJ5ZKHhwcOHjwYz58/b7KfoCYDEf8OdCpt8uXlEpUhNOjLw507d1gfa9SoIdNjWv5wUWxleiKD6jUeKE+fOXNG5CdQ3qQLXYIgiBYZpbz5+++/IyFEJq953lSKpfDy+uDBg6wsz+9SMBZLoSDVcRRu3Lgh0nHG7DpeDioBlVvG+qMGdNHn2LFjiulUngqCIIppSH1Hqfzct2+fyJ5RA37etWSPWuyNt0Ok+hgRZXqaj73xskCqp58/f44rVqzAhg0byq78k/J0cQB9Y1R6QwRvJ/NXTarBmTNnkBDDJjtzQWqjSIHyLfX7qY0i7QOlcyU7BBEZ31pYWOChQ4ewV69eis9A0Fjtpk2b2JuYiNqynMY8xowZI0uT+qmm+qZKslD6rzROoGbr8/nUZKfSGKXyR4o3c/xtrU9pjEr4pjwifa9Nyc/Tit+Y6udpgZa9qAa8HFG7xjQlJQUnTpzINqZL6bRy5co4a9Ys2WJvcYMAJfDWw6NHjwAAoGbNmkbz1qpVCwAAHjx48Fpt0vKVK1fWzEfTU1NTobCwEAAAYmJiAABAEAT48MMPAREhPz+flfHy8mL//+uvv2R1tm/fHgAALl68yMY+a9YsePToEYwfP57lQ0TIzc2FLVu2wMCBAyEjIwNcXFwAEWH69OnQv39/xT4/f/4cAADKlCkjS+P75u3trTn2oKAgAAB48uSJZj6KFwsLC8X0UqVKsf/rdDrF/zs6OrK/EVGxHkdHRwAAyMnJ0exPcQPFw4kTJ0S/e3h4sP9nZmYarYfmsbe3Z7+hYYOA6DMljQealpaWBgB/zzuPXwpubm6MBvLz8+H+/fuydEIIAABcuHABAP6mmT///BNatGgBAADXrl2TzZ8xoHnoOHx8fGR5Hj9+zP5P8S4FOl7aT/43LVwZw2N4eDgcPXoUIiIiWB61vMZkFi9feJkVFhYGAACHDh1i6QAA6enprKyfnx8AiOXLgQMHRP2cPn06+3v69Olw69YtUX1qwMszqWzi26M8nZ2drVmfMaDlLS0tjeZVk8l5eXkAAODg4AAAAOXKlQMAw9zQNAq2trZsDFKg9Jefn8/KtWzZEgAArK2tWRoP9erVg4ULF7K/09PTAREV5/3Vq1cAAGBjYwMARddVptKuUh6eF/j8Fy5cgOnTp8PDhw/B3t6e6Y4XL17A6dOnITo6Gm7evAkABjluTNa/TfC6ctne3h78/f1ZfkEQGB49PT3B399f83N1dWV1SedeCUyV9Vrl1ODf0o0UYmNjARFhw4YNot/LlCkDzs7OAACQlJQkSnv27BkAiO2QixcvsnIUqDykIMV1aGgoAADTf6YCLZ+bm/tGdbwSUP1hTF5LdZKW3WcKKNGgsU9aloK9vT0kJCTA/v37ISUlBSZPngxhYWGiPCtWrIDKlStDVFQUzJo1C1JTU9k8ValSxWh/o6OjAcCAr2bNmsHly5dh4MCBkJeXB2PHjoVKlSoxXVrcINXVFAghIAjqrqUULz179mRpiAgFBQWAiDBixAgRXlJTU1k+Ka8A/G2HOTk5weHDhxlf8TpLaodJbUI+Dy8rpDZhVlYWAPytc3mfhU+j6QCGOeJ508HBgcnTGzduyMbTqFEjADDYlVLgeZPSU6VKlWT5pKClc3n/Ijw8HAgh0KJFC7h9+zZ8+OGHsvyzZ8+G6OhoqFSpEsycOVPRl6Nz5uzsDF9++SXTqVevXoW4uDjo1asXPHnyRFW+8HpbSb5IfSupfFHzragPCCD3rZRkq9Q2pzKZl61aMlkK5spkU6Fv375w9epVaNu2LaSmpkKPHj2gUaNGkJycbHadVlZWAPDP2rzmAKXtOXPmwKNHj2DVqlXQrFkzkUzav38/jBs3DgoLC8HW1hYKCwth8ODB8PTpUxAEAXbu3Amffvopy4+IkJqaCrNmzYLo6Gj44IMPAMC4n6AFlHajoqJkabxcksoQHry8vBRliCn+sCl2E82fkZHB+lyrVi0Rv9vZ2QGAAe+8n0DHx/Mtbz9LefPp06cAILYRpbypFEvh5TX/u5J/L00zNnY1HVemTBlGT6bYdbwcVAJq1/Hxp6IA7YvU36PA68CCggL2f6nvSNMoTfF2Gp1bJeDnXUv2aMXeeJDG3qR6Wi32JtXTdnZ20KNHD9i3bx/cvn0bunfvzvJKeVpLj5kK/v7+AADw22+/iX53c3Nj/zfFBykOGSm1UaRA+ZbnT16+PHz4EAD+pgUlOwQAGN/a2tpC3bp1YcmSJTK5y8dqO3ToAF5eXvDJJ5/I8CQFOhf16tVTzUP9VE9PTwAw8IKWXyr1YXk/VQqm2vrG5IgUpPKHx9vEiRNV+0IIAQcHB5P8bzVfXAvU4npKfp6WLWmqn6cFWvbi64C/vz+MGTMGrl27BidOnIC+ffuCq6urKCY0ePBg8PHxgVatWsH69etfqz01KFl8+w8AZVQ1xc2Dk5MTAAC8fPnytdqkypw6i2qQm5vL/k+FGt92xYoVRfl37NjBhLq0PAXquDx+/JgZW/n5+WBlZQXZ2dlACIGIiAiYMWMGVKpUiTFNRkYGPH36lP29cOFCSExMlAkSLRzp9XrF/ysBdUZoH9WgdOnSAABw6dIlxXRfX18AMAi8q1evst+lBty1a9cAAFQF6esacOZC69atGb55nDo4OECNGjUAAEwSYDRPSEgIG0OrVq2gsLBQ9Pn6+oIgCLBo0SJZWmFhISxatAgIIUAIETnNdD6psqD45BXErVu3RPQpNX5SUlLY/6kS4AOr1DhMT0/XDKwqAZ0/yntKASzeuJYGsqU4+/3331kaxceYMWOgsLAQkpKSWJDM2toaBEGAkydPQmFhISxbtky1j2XKlIEjR45A1apVAcCA11GjRsl4zJjM4uULz4/t2rVjtJSWlsbkAx/Q27ZtG6uHyhepAbB9+3YAMNAgXfxHRKOGLy+PlOSDVJ6tWbNGsz5jsHr1agAACA4OhoCAAAAwyEglUJPJVB788ccfACDGj5OTk2hubt68KRoDn3by5EkAEM8ZnRu6AKU0n9LgASLCzz//LMtH5dzBgwdFdRvTVVK6njp1KhBCICwsjNHF5cuXZXJAStfLly9nfLBhwwZo06YN6PV6Rh9ZWVki3TF79mw4efIkfPbZZ6wvoaGhmvzxNoG5cpnSYe3ateHWrVvQvn17IIRATEwMVKhQAQRBgMTERLh165bmN2PGDFandO4pfwYEBDDHtEmTJrI6OnbsCIQQaN68Ody6dQuGDRsGAABly5YFJycnEAQBtm/fDrdu3YKvvvpKFRf/lm6kQJ3+jRs3ivhbEATo0aMHICKMHTtWxI/UaaYyPysrC7788ksghEDdunVZPmqHUJDimjrZSraWFtANDT4+Pm9Ex2sBb/cZA14nadl9WqBlbxj7eBmkJht8fX3h888/hytXrsCpU6fY71TeJCUlwZAhQ8DHx4eNWS1owgO1XahOsLW1hZkzZ8LJkyehUqVKcO3aNWjQoAH07NnTaKBfTe+ogVRXU54ODAyEBg0asPFpga+vL7x69QoIIRAdHQ0ff/yxaIHs4sWLDC8vX74EQogirwCI7TA/Pz9wcXFhaTyeeaA6iLcflGSF1CakNj21IXmfhU+j6QCGIDHPm3waDZTxQOuhgW8eeN6kfpdS0DAgIECEJ7qJSUnn8v4FP7dWVlbg4uIChBAIDg4W+SuICJcuXYJhw4aBv7+/zIZZsWIFAABEREQAgMFWOHXqFMycORPs7Oxg+fLlEBoaCmXKlBHJF0pL7u7urH9K8kXqW0nli5pvxfOW1LfibX4qW6W2OZXJvGxVKqcG5srkooCXlxds3LgRNm3aBGXKlIH9+/dDxYoVYdy4cWa1SwPexWnzvgmgc6PT6cDKygq6dOkCO3bsgPv37zNeobSbmZkJL1++ZH/TxWwnJyfo3bs3sxf79+8PpUuXZvkuX77MFoNM8ROUgPaF31RAgZdLvJyQzhsvl549eybTY2fOnAFCCFhbW2vayl5eXiAIAvzwww+AiCJbuX///iJ+4fkd4G+62Lp1q8hPoLy5detWVnbfvn3s/1Le3Lt3LwAYZJZa3EMplsLLa/53nt+lYCyWQkGq4yjY2NhAgwYN2Bwas+uWL18OAH/LQR54u65OnTqa/VEDuvBw/vx5xfRz586x/9NNwwBy35GmUTxfv36d5aVzqwRS/1BN9tDFUSWe4TdaSGNvUj2tFnvTit14e3vDoEGDgBAClpaWMp6W0rU50Lx5c0BEWLx4sWiR08XFBcqXLw8AALt27TJaz86dOwHAYEuZC1IbRQqUb1euXMl+420vnm8BlO0QAGB8y+NNKnelsdr09HSYP3++0QMlFId0LDxI/dTdu3cDIQT0er2mX+rj4wOCIMCECRNkfqqXlxezlwRBgPPnz6vGHleuXAmEEChTpgwUFhayeASAfLOvFNTkj5WVFej1emZr8enU9n3+/DmkpqYa9b/VfHEtoHy3bds2o36eVvzGVD/PGKjZi8UVg4mJiYH58+fDw4cPZTGhvLw82LFjB3Tt2rVY2pIBlsBbAVpHQaOiohSPMisBPV7u7u7+Wv2JjIxEQRBwypQpmv3csmULu7bB0dERAQzvZlGgx08tLCzYcXD6PgUAoKenp6zO48ePIyGGq+bo20qrV6/GDRs2sGOo/F3rSUlJOGTIEHZVCiEE69Wrx+rt3Lkznj9/nv0dHh6OgmB4eFgJtOaCB3rFgbF7w+lj40FBQfjixQtR2osXL9gbRgAgez/u008/ZdfZxMbGoiAIim/MPXv2jN3J37NnT83+FDekpaWxI+5NmzYVXU+we/duJMRwj7vWXdf8Q/dff/01e/fEy8tLltcYPoODg9k1p/xj9PSLi4tDRMQ6deqgIAhYqVIlltakSROcPHkyEmJ4e016J/igQYMY7ZYqVQoRxVczfPnll+y6BUTx/Gk96snPH33zTenqGh8fH3YEXUqfUpwdO3aMyQL+mqHBgweLrl7ieQxR/DirGmRlZYmOw0t5zJjM4uULL7NevHjB7pKOjo7G77//nskXT09PBDBcI/XTTz8h4t/yhV5hU1hYiBMmTGD9mDNnDtarV4/1NTIyUtRPKfDyTOmtOtoefYuK0qs5QB9EFgQBv/rqKxwzZgyrm39YXQlnPDRp0gQJIVi5cmVcv3696CHzhg0biuZp5MiRbAz874IgYEhICBJiuDKVXjs5efJkRET2VlyDBg1k8pHOn5ubm+gqGSmdDR48GAkxXDV46tQpk3UVT9cnT55k/DFkyBBNWW0KXaenp+PcuXOxevXqmrpDiqu6devilStXRO29bddOmiuXO3fuzPSvVC4bo1EeeHzzcz9t2jSRHm/WrBkSQjAgIEBWBy9bv/76a9HcS2Wrmtz6N3UjD5SObWxscObMmez6n2fPnrGrspo2bcquIaS/bdiwAX/99VesVKkSk0306i4l2pfy2eHDh5EQgj4+Pib3Vcpnb0LHa4GUd02BrKwsTbtPC7TsDWPAz4MpupMvM3fuXGzfvj27RpTKZUGQX/GpBHRulfpdUFCAX331FbsuztXVFb/77jtRHv6aFlN4mgdeVwcFBbG5HTt2LJ4+fZrV++WXXyqWl+pq2re8vDzRm7T8JwiG9xmkvIIolhX8VaT0qm4AwMDAQExKSmJpDRs2REEQsGvXroioLiukNiF937N69eqYl5cn8ln4tPj4eNH88LxJ5R4AKL4FRK9T499Lu3z5sow3nZycEADQ29tbZg+PGDFCpLuoHnd3d5fJD2oPN23a1OjcWlhYoIWFBVavXl10TTyvH/i3DaV0h2i4Mq9ly5aM7qlOqFy5MmuvQ4cOrP9K8oX3Bb777juRfNHyrcqVK8dwr+Rb8bjhaYJeq0n5RCpbTfUdzZHJrwNZWVnYr18/9jZhSEiI7Co9Y/YL9W2K0+Y1F7TwTK/t3bhxo2q5zZs3y2IGERERjFZq164tu7I4Ly8Pt27dih07dhQ9VaDlJ1CZpdRPV1dXBAAMCwsTXcOGiCK5RK+0BQCZnKdyiaZJ9Rgvl7RwxvsQUltKeo0m/5Yd4t9vfer1evbGZIMGDRhvAgBGRUWhhYUFBgYGYmpqqow3a9euzWTaxx9/rBr3kPr+PG/Gx8eLfH+la3gp8LJOC6T+6N27d1kalYPC/7/KTc2uozgghMjkoJJdZw7QN9A8PT3xwYMHorQHDx6gp6cnw2+HDh1YGj/vr169Ym92tm3bFgsKCrBKlSrsOraRI0eqts/7h1LgZQ+d79GjR4vyUNmjxi9SPc3H3ni61ordIIr5AVHM07weMxfu3LnDrvJMTEwU9eGnn35CQgh6e3srvllLYf/+/WhjY4OCIOCECRPM7ovURpEC5Vsl2fLtt9+iXq/HKlWqsDfSPDw8ZDIkKSlJxLfGQBqrpbyjJpciIiJQEJSvU+X9VETxEyVaIJUhvKxr2bKlyNaUyhC+LH2Tm8qQAQMGKNqriHJ9pSZ/pLYdHaOLi4sMb0UBfoxa+I6MjGT9NubnqcVviuLnFQV4e1ErBkNxb8wGUwNpTOhNxXNKFt/eEqCT3K5dO0xMTBR9ZcuWRUEQcMGCBUbroQ8dvu6bbwMHDmRMzxM47Wf16tUxNjYWbW1tEQDYm0XUyJg4cSIWFhbipk2bWGDNy8uLCXr6Kb09sHjxYiSEYIUKFfCdd95BQgh7UJY6o0qPdxYUFOCuXbswPj4es7OzmXMrCAIzCgVBwBYtWqAgqL9HlZKSgikpKYoKiwf6KGr16tU18/FOTXBwMM6bNw937tyJ8+bNY3ca8498NmvWjBn0z549Y+/iUdwuXLhQVH9xGXCvA2vXrmWOnY+PD3755Zd45coVLCwsxJkzZ6IgGN7Jatu2LS5YsAC3bduGmzdvxpkzZzJFRIjh7ZKXL1/ilClTGM7u3bsnassUfA4bNowZZhSvvLFP33wQBAF79eolC64TQkRGal5eHk6bNo2lAQBWqFCBpVOnhzpllSpVQkT1wCoP0vmjjs7cuXNleRs3boyEEGzSpAlmZmaK0qQ4++GHH5AQw7sdaWlpWKVKFZbeqFEjkcEhCAJ7q7EoAUSelylOAcCozOLli/TuaN74pG0EBQWxPlM6i4qKwnbt2iGA4a2xTz75hL0VRAjBatWqYUFBAWZnZ2NAQADrG3WIpONLT09nD1BXqVJFsd9UngUGBrLFKkEQsHz58jh9+nQ8deqUbF4oZGZm4qlTp3D69OlYvnx51s/Q0FB8+fIlpqWlscCiIAjYvHlzXLt2LT569EiGM14m00fGpYYf7SOlawsLC9TpdPjee++J8vn7+zMDG8DwGDV9x61+/frsnRgaMJHqqjp16rDgGI9nKZ3dvn0bnZycUBAMb4DShcHw8HBNOqMOFR0DNQD59zmVDK2i0vXVq1dx1KhRmrpjzZo1bMOIpaUljhgxgr0t8bYtviGaJ5cTEhJE+oaXy8ZolAce32fPnmW2Av1KlSqFjx8/xrFjx7J80nvWCwoKsG7duqJyTk5OeOfOHZlsnTRpkgz/b4NupPD06VOMjo5mfXRxccHOnTvjpEmTcPz48UzmCYLhLQr6gDd9z4PKi88//xwRDQGIgwcPyt42kfJZXFwcAoCqTKNQUFCAp0+fxr59+7LNBZTPEItfx2uBlHdNBS3e1QIte8MY8DKoqItv1atXx/r162OdOnWwXLlyzIYWBAG/+eYbo21TW7lixYqqeVJSUrBp06aszVq1arGHxaVvrRjjaR4ePXqEY8eOZUFZAEBbW1tcs2YNe/OGypCoqCj87LPPcO7cuThr1ixVXU2B5xXKu7SuGjVqyHglOjoao6KiRHoMAHD48OGYlpYm2mDl5ubGxktteEEQMCEhgdELLytWr17NArrUJty2bRsr17RpU5w6dSrzWWgaP376rtPt27fZO5d8P5V8CKprY2JiWFsUZ3SxtHPnzuju7s7akdrD/Dt5fHu87XLq1Cns1asX+3vhwoVG53bFihVsDipUqICNGzfGsmXL4pQpU9DOzk7UVlBQED58+FCVjtasWcPGwH8ODg44ceJEkV0ulS9Dhw6Vja1MmTI4c+ZMTd+qUaNGKAgCW5SQ+lZUtu7cuVOkP6pVq4aCIODMmTNF/BcdHS0qZ0y+Scv9U3DixAmsUKECw2fXrl3Z3BizX16+fFnsNq+5oGX3NW7cGAVBwEGDBhktx8cM0tLSRHRYs2ZNVT+B4sHOzk6xf9RPoHKGynn+o++oEUKwadOm+Oeff7LyVC5JaZuX81Qu0TwxMTEyPcbHUrRwRvMJgmHD6sCBA1nfqO7+4IMP0N3dHS0sLDAhIQFr166NiAb9YmtrK+qrr68vs18ADJusK1SowOw3+p6tl5eXaHyEEDx37pxq3IOnUW9vb/a2Nx8jor6/IAg4adIk0VuRmZmZMllnDHh/1N7eHvv27Ys7d+5kcpBuOKT9sLW1xaioKKxRowabY/rxusrZ2VnRrjMHdu3axcbk5uaGQ4cOxQULFuDQoUPZpqnJkyezjee9evXCBw8eiOadfwdzyJAhivP+448/itotKCiQ+YdqcOLECUYThBBF2UMIUYy98Xp64sSJOG/ePBZ7o7/PmDFDpqelwPODFJ4+fYoLFy5kdG0uzJkzh/WpcuXKuGbNGhbPo4vK9vb2OGjQINy5cycmJSXh77//jhs3bsT33nuPvXno5eWFT548MbsfUhuFly+IiOfPnxfZSzY2Nli9enX2bp0gCLh+/Xq2KYH63pRvRowYwd4o0+l0eO7cOZP7xsvd/9feeYdXUXwP/+ymd0JCSYEk9I7SkdAFpIN0RQFB7IoiCKII+rWgYv+pIFVRQUUFEQFBiog0qQLSRI0gXTAESEJy3j/yzji7d3fu3s3dm3vj+TzPPoQ7uzPnzJw5c2ZmC8vzvvvuw6lTp2oONmbMnj3bJQ/xRo6jR4/ixx9/jIqiYK1ataRl69cPhw4dym2SxYaKovDvPYo+RLRTdt59992Hb731Fl/70Pvsq1evSmMt2bxLr6NYb55gtPlmttfA0t3N8xo1asR9vd15nh3crcEUd/NNhK0JOQFtvvkJosGbHX369HGbz5133omKomDfvn2LJc+xY8cwLCwMVbXobr/Jkyfzu0bERXEx6BD/zyaKbCc9PT2dLwSbXdewYUNs27YtvyNMDCzZUb58edyyZYslHfLz8/ndH6LTmTJlCiqKgjfffHOx6ujmm29GVVXxjjvukJ5XWFiIrVu3NmxjRSm6c2D//v38qSZxIEhJSXFZUChfvrwjAVxxYYGoqGdYWBhWqFCB39ltZtuKUnQX0y+//IKIiN9++y0/X38Ho5X6PH78OF+8EBc49HUZHh6OvXr1crFLNvm67bbbsFevXli+fHmePwuORo4cyWVii0Xs2rCwMOnCqqz9xo4di6padOeUHhbopqamujxJp6+z0aNHo6Io2L9/f0Qsmni0atXKpT+wekpMTMS8vDyPFxCN6s6dzxL9C9uwbNOmDR49ehR37NiBd999tyZvo4mo/jemE1vUEBd8Dh8+7CKroii8vFdffVXz8WajYA/x3w2w7t274+HDhzUbeeIRExODSUlJmJaWhklJSfxuJr2t1qxZE48cOcLz37x5s+audHbEx8fzD8QCFN19Wr58ec0E18y3ivVidq7sYNeWL18ez58/7yKbvg1UVeUfshftDBFx6dKlfKLOrgsODsbrr78ee/bsiUOGDMFhw4bhkCFDsGfPnnj99dfzjUB2REZG4rJlyzQ2aBRo2bVrRPOxAxHxwoULeOedd3JdMzIycNmyZX65+YbouV/W24nolxHlNlq/fn1s2bIltm3bVrPoaZQv6yfiAnHHjh01bR8TE+NyXXR0tKFvFc/zt7GRcfHiRX4nu9l4aObrIiIiLD8tYKefiT6KLSKxfsbw5hgvQ993PUHWd82QxRvuEH2Qp2Mnax+jo1OnTm7LvvXWW1FV/70jXMaHH37I714OCQnBRx99lD9Zzvy7uz7dsmVLrF+/PsbHx0v9v9FYa2bn+rGaoe8rYjn6+jMrk21wP/HEE1ipUiWXse3666/HsLAwl99btWqFDz74IH+Cn030d+7cyeXr2bOnSwxUoUIFvoEn5le1alVs3bo1NmjQQPMEADtCQ0Nd+iZbCDLamBLr2qz+WVp8fLxmk0k2BwsNDbXUtvo2lI0fVmzJSDZ9m1sZq/T1Yja3YjGF0fxSFptPnDgRFUXBW265BRERb7vtNlRV1ePFMLvXeYP8/Hx89tln+bhZpkwZfOutt/iTGTLf5UTMawdZ3CebI8muQ0Tcs2eP4dhrd56gj7/1h95ma9Soga1bt+brJvo+WrduXRe/xI5HH33UZRzr06cPqqqqWfA10j0vLw/r1q2r8ati2dHR0Xjw4EFu/6qqYq1atXDIkCE4YMAAzY2XTB+jPmllviFb92jUqJHL5r5+jiLO/VVVxbi4OGzZsiW/qZDpV7VqVcuLw99++63m6UH9YUUvva9k//ckrpPRr18/03G2atWqeP78eZw7dy4/h71Nxmg8Yte5a3f2lKk4P5Qh3mjnie+5evUqn/+qqspv5Lvuuutc6lc/TouI/cFJxBsBWV1XrVoVmzdvjhEREW7j5Pj4eMOnvTxFjFFE/9KgQQO+mWYWUw0fPhwRkd+4JDv3kUcesS2jrC7YYTROsqfHVbXoDRkPPvggKoqC3bp1k5anXz80iyFuv/12Fx/SpEkTU9+mKArGxcW5rFeY+cXIyEhs2LChdN6l19EuRptvsqNRo0Zu53lmsaEn8zy7yNZgmEze2HxzEtp88xPS0tIwPT1detSsWdP0DjPEort+2R1FL774YrFlmjFjhktHNQuW2rVrh+3atcNmzZppJkl6Z23kxI3OMZoYtWzZUvPYvxUKCgpwxIgRmsDn66+/5gOc7FWAMv766y++eTBv3jy35//999/8qSXxqFKlCr/D9uLFi5iWllaiAVxx2b9/P3bs2NFFT6MJuxgs3XvvvXjmzBmez4ULF7jdGz26bKU+ERE7derkElBaDZSNZI6JicGoqCiMjo7GRYsWaWT65ZdfNHczmgVW7tqPLeIbvc50+/bt/PrPPvtMkybW2bRp07gvEO+gv3z5sqbeVFXF/fv3879nzZqFu3btwilTpuCUKVOkbS36rOjoaE39WfFZzL/I6hzg39ecGPUDo8nXiy++iFevXnUp75133jEMhvRl9ujRw1De3NxcfscNs8ns7Gx88sknsUyZMqYTbKOjTJkyOHnyZM1j/YysrCy87bbbuN5iW1m1X1YncXFxOHv2bD6xtnqt/oiKisI1a9a4tDu7wxSg6MlH9nuNGjX4Kyr1k6odO3Zgq1atTNtdNrGtUqWKZmIlW1Cwa9cMo7FDZMOGDVirVi2exu7UNZtAliSe+GWxXdq1a6fxywyZjcp8vaqqfJFfPNz5XQDQXGfmW40mhv4yNops3LgRBw0axCfiRgerk6CgIHzkkUf4E2hW2bFjB95www2W+5lYdps2bUwXMLw1xsvQ911Pcdd39biLN2SIPsiqj2nbti2Pmc2ODh06SOP9Cxcu8In766+/bknW8+fP83phE2r2t9U+rW/foKAgHDhwIA4ePNhlw1ccm/WHbKwWYX1Fv0lmlGdaWhred999ePPNNxvKb7bI6C72CwkJwTlz5mjkys7O5k8Ums1hZHFNaGiotG8aLdgAFG1CJScna+aGlSpV0tQ9O1g8nJWVhbfccovbcd5q2+pjEv2RkJCAjRo18tiWVFV1iSWNYjW9nxT/1euO6L25FXuiqWLFinjx4kXe/6w8QcOwe523OXz4sOY14uINUzKciHk9RfS5emRzJNl1jDfffNNr84SqVatKfXxmZiavd3e+Su8nmF9q2LAh9ujRAzdt2qQZxyZPnoxlypThc1V3uv/+++98niD6tLi4OFy6dCkiFm3Smd34ymKVmJgYjI6OxsaNG+Prr7/O316kz1ffb632TaM89P0dEXHAgAGmfbpChQqGb6GRcebMGRw5cqTUD8pkNxqr7MR1ZuTm5uLo0aNdfG6rVq3w0KFD/Dz26mOj9hDltdruiqJo5ocy2NpbXFwctm/f3iPf88svv/CNb739sMNonGYcP35c0x+cZvXq1fwJWavjn6IUbR6J7VUcxBjFKA5RFAXLlSuHrVu3xrJly/J++95772FBQQEeP34ce/bsiS1btsRy5cq52ElUVBQ+88wzxZLRytgRHR3tEgtfunSJ+9EZM2ZonlRzh9n6IXtDG5sHGPkQs77N/M+OHTv4U2HuYi138y5Rx/fee892HR85cgTnzZuH8+bNs7zXsHXrVuk8z6gOPJ3nFRejNRgml79vvimIHnyVnPBrVq5cCS+88AIAAEyfPh0aNWpU7DwXLlwIY8aMMfwocHBwMNx9993w8ssvaz5ueubMGZg6dSosWLAA/vnnH/57SEgIdOjQAXr27AkbNmyArKwsKFeuHGzcuJF/tDEvLw/Cw8MhJiYGUlNToU6dOnD48GEICQmBatWqwezZs23pMXbsWPj8888BAODYsWP8Q55BQUG28luyZAl8+eWXAADw0ksvQWJioqXrfvrpJ9i0aRPk5eVBzZo1oUuXLvwjtAAAiAhjx46FuXPnunzgVDH4iGflypWhX79+MGbMGP4BY3/g4MGD8Omnn8LmzZth//79cP78ecjJyeFtW6lSJahbty60adMGevXq5fbDx2ZYqc+3334bZs+eDQcOHIDg4GCoVKkSREZGwv79++HKlSua/Dp16gS1atWCtWvXwr59+/jvSUlJ0KtXL3jsscf4h17N+OGHH+DNN9+EpUuXwtWrV6XnGrVfdnY2bN26FQBA81FnRps2beCvv/6C2rVru3wUlzF//nyYMmUKAACsWrVK8+HT/Px8GDx4MHzxxRegKAoUFBTAsGHD4MSJE1C5cmWv9TErTJw4EaZPnw75+fkuejL/8sQTT8CqVatg8+bNsHjxYrh06RIoigKpqalQUFAAiYmJkJmZCV27doU2bdqAqqqm5S1cuBBGjhzJ/Y1Yppk/Y5w/fx7Wrl0LAACtW7eG8uXL87Rr167B2rVrYePGjbB//374888/ITs7G65everizzIzM6Fdu3YaOzXi1KlTsHTpUpc8mezi0K38/4/xBgUFQf369aFly5ZQp04dGDJkCCQkJMC1a9dgyZIlsHLlSli9ejWcOHECcnNzQVEUiI2Nhe7du8O4cePgww8/hLlz58L58+cBEUFVVUhKSoI1a9ZAzZo1XWSUjTlGdiZe9/jjj8PZs2chMTERsrKyNB80ZyQkJPA669OnDzRt2lSTPnXqVAAAuO+++wx9sNN2nZ+fD88++yy88MILkJeXBwDgoqs/4W2/bGajZnbfq1cvqFChAmzbtg2+/PJL2LhxIxw4cMBy2xfXt/ob+fn5sHv3bsO2qFixIsTHx0NycjL/OLqnLFy4EF566SU4e/YsJCUlwa+//mqrnxnh9BhfUmOSp6iqCoqiwN69e6FOnTqOlGHEunXrYP78+QAAMGHCBEP/LLv2rrvugsOHDwOA1mfZ7dMAABcuXIBly5bB5s2b4f/+7/8AoKgfxsbGutiEu7FaT35+PjRv3hxOnDgBiYmJMGTIEBdbq1y5Mj/fzFfox00AgJSUFEhISIBjx45BdnY2T2dzlilTpkDz5s0N5frss89g/vz5sH37djh37hxER0dD7dq1oVevXpCUlASffvqpYdo999wDBw8etOUHzZDFwytXroSpU6fC0aNH4erVq3xuxuogMjLSctu66++sbe3YUtmyZeHZZ5+F5557DvLz87lucXFxUv9y7Ngxx+dWeXl5kJWVBQAA27ZtgxkzZgAAwAcffACpqamm7SKycOFCW9c5xbx582DcuHFw7tw5ALAevzgR81pl+PDhoCgKPPfcc5CUlOSSbjZHateuHSiKAvPmzYO0tDTT/BcsWACjRo2C3NxcAPDePMEMMx/SoUMH2LlzJ3z77beQl5fH5bDil/RY0Z3NE8R+xOYQjMuXL8NTTz3F5wkAwOcQkydPNhyHRL9UpUoVQEQ4fPgwBAcHQ0ZGBnTo0AGefvppj/pmZmYm1KhRA6KioizP/TMyMqBnz57wyCOPaHTyBCt+sFatWpCcnAxhYWFw4cIFQ78ljlXe5PTp07BlyxbefvXq1XM55+uvv9bUS1paGqSmpkJERARcu3bNa+1uhLj25qnvOXPmDAwfPhxWr17N+wMigqIo0KZNG5g2bZrl/uALEBFWrVrF4+RffvkFCgsLNeeUK1eOj2P9+vWD+vXre10OWYxyzz33QGxsrNs88vLyoGvXrnDx4kXo3r07NGzYEDp27AhxcXFel9dTli1bBvfffz8oigLvv/8+tG7d2tJ1dtYPmQ/p0KED7N+/3/TarVu3wpNPPgmbNm2CS5cuacplPs3OvMvXGM3zmD5BQUFQvXp1aNasWbHXcu2iX4Nh/uD06dOW1+VLAtp8I9xy5coVWL58uYvj7tq1q3TiUFhYCIcPH+bXVKtWDSIjI30oeWAjW5hzOoD7L1BYWAhbt27lC1hs0s+4evUqnD9/HqKjoy0FJ3qo/axh178ESnlO4A0dTp06BcHBwVC2bFmXCa4szSlyc3NdFm/CwsJ8UnZxOXjwILz66qt8sWbu3LklLFFg4Wnbk2+1TyD3M3/k999/B4CiDZzg4OASlsY6eXl58Pnnn3OfNWzYMK/m7y/14omvKOk5C/XNf3FyTKXxQ8vZs2dh1qxZvK6feuqpEpao5PGneUJJ+yUznJgnUN/0f7zZ7nZ8j7/2B3cgIly8eFFj00Yb+ETpg2I7Zwm0NRjafCMIgiAIgiAIgiAIgiAIgiAIgiAIL2H9nR8EQRAEQRAEQRAEQRAEQRAEQRAEQUgJnHekEH5NQUEBHD58GLKysuDSpUtw5coViIiIgOjoaKhUqRJUr17d5ftqx44dgzNnzsC1a9dAURQIDw/XXMfeQe1Lrl69yt9df+XKFYiJiYH4+HifvWbin3/+gZ9++gnOnDkDwcHBkJKSAo0aNfLau/JLmkDUj8kaHx9f0qJIkclplnbx4kU4dOgQBAcHQ3p6ul/rWNJ9k3CPE3Zmt//J7OXKlSvw559/uoxVJTHmlASBon+g+F6C8AZ24uiSyNOfyvMlbM7CYlf2PTQR2bjjixhGFmMXJ/4+duwYnDx5Ek6cOAEFBQUQEhLik7YtDbGrP1PcOZm/93er+hnZmZX+7gT+MNcpCd0DqU/7QxtZwZ/lZH3z1KlTcPr0aQAASEpKgvz8fMd8SEn16f8aJbXW52/jkRM+zSkdZesCABDQawb+ZheARECRm5uLH330Ed51113Yo0cP7NGjB44ePRo//PBDvHz5sk9luXDhAr788svYqlUrDA0NRVVVTY/Q0FBs1aoVPvnkkzhy5EiMiYlBAHA5FEXh1wQFBWF6ejreeuut+Nlnn2FBQYFUnl27duHUqVNx6tSpHqV9/fXXOGLECKxatSoGBQUZyl+mTBm88cYbcdasWZiTk+NRPRUWFuLFixfx4sWLpufs2LEDu3XrhsHBwS5lR0dH44MPPojnzp3zqFxf4Qv9zp07h9u3b8edO3dimzZt8J577sEff/zR5bz27dtr0i5cuIDz58/H+fPn83MKCgpw8eLF+Oijj+KDDz6I//d//4etW7fWXJeTk4OPPvooJiYmcjnj4+Nx6NCheOjQIbtV5RFGcp4/f16T9tBDD2GjRo0wKiqK950yZcpgo0aNsFOnTli1alWMiIjgaUyHH374Afv374/BwcG876mqipmZmbhhwwZL8unr2gmc6JsyX2CHvXv34ttvv42vvPIKLl++3MVP5eTk4KRJk7BmzZoYHh6OiYmJ2L59e5w3b540X9Hms7OzTc8bMWIETps2DY8fP46IWpvXp4lYaT+ZDV68eBGnT5+Offv2tWRnYpvVqlULBw8e7JKnWGes/zH7jImJcdv/zOxFURRUFIXbOfu/3pZkY87Zs2fx77//Ni3biJycHFy/fj2uX7/eo+u8TUFBAX722Wd46623Ynp6umlf0uu/e/dur9o2s6fbb78dmzdvjt27d8dbbrlFY6P+4Hv9lXnz5vF2MkPf573h73zh6/9L5YnYiaOnT5+OFy5c8EqeiqJgxYoV8cEHH5Tm6WsdrCDzyWZ9xVOfnJWVhffee6/GH7GjevXq+Morr+CXX35pGqe4G3cURcHY2FhpDKOPsUUdWNqGDRtcYmw2doaFhWF6errH8XdWVhaOHDkSIyMj3c7XrNqSEzFFUFAQZmZm4rJlyyzFTFbmCU7i7RhUj9NzspLq757ot337dmzUqBHvf2LfY/plZWW5xBpxcXFYo0YNjI+PN+3vubm5Hsvsbp6wePFibNCgAYaEhJj2MXdzHbvjGIsZtm3b5tbXeao769P9+/fHXr164bhx4/DgwYOac/766y9s0qSJZi7K+rQ4Hy3JcRrRubUiK3GdE3ImJSVhv3798MiRI14pF9G67+nUqZNmjJKNK6qqYlpaGk6ePNmWD7Eyhtvp03pk8+2SxFtrFEZtq7ddT8cVb9SZU/Gu2RqMkU+7+eabuR4XLlzA119/HZs2bVqsNTa7Olodc2XrAuKaiaxferpO7ymyeJFhtmYwcOBAfOyxx0osTpFBm29+AptIyTbQVqxYgampqabGk5ycjF9//bVP5H3vvfewbNmyLhNMfUcVfzc6x+xg14qdvWbNmrhy5UpTmebNm8fPt5L2ww8/4HXXXefimM0Odk5CQoJHk7S5c+eiqqpYvnx5Q8e0YMECDAsLk5avqiqmp6fj4cOHpfp7M4CzipP67d69Gzt27Gi4oKCqKtaoUQP/97//4e+//46IqGmnGjVq4AMPPIAAwOvkjz/+wAYNGrj0HZYfC8bS0tJM5YyJicHvvvvO6/Uotp+ZnPHx8Th37lyeZrU/6fuVrN+qqopvvfWWW3n1dS22Q3HxpG+KclvpmzI/4Q6xjXJzc3HIkCEubVS3bl385ZdfEBHx0qVLXA8jX9KlSxe8cuWKpgwjmw8LC8PevXvj7t27XWRi+QUHB2OnTp3whRde4DavT1uwYAHOnDnTxeaN2k9mg2+88QYmJSVJJ096OzMbI1iey5YtQ0TEvLw8zMzMNLXP0NBQ/Pjjjy3biydjjV7uqlWr4k033aRZiElKSsKHHnoI//rrL7f28vPPP6OiKD73ySIrVqzAGjVqWBrjzPqVN2xbtCd9uzAbvfHGG7FGjRpe970lNTZ6Gyu+S9/nR48ezevYLk76+v9ieQwxjtbHyu7i0LJly+KsWbOkeVrp7/p+2KdPH49097Q8KzqI6Pvu8ePHceTIkW59sllfEX3yp59+ivHx8Zieno75+fku5a1evVrTPlb8pNVxx+yIj493iWH0Mbaow9y5c3l5srbVj3n6/+vj79WrV5tuurmb65nZkhMxhZEuVmMms3mCOxssLsWJQa3g5JzM6f7uDf1ef/11S31QXwfuYkOmR5MmTfDUqVOm8nkyT/jhhx+wQYMGlvsZu95oriMbx4x8nf46K+sw7nQX2bVrF+/T4hEaGorvvfceIhYtZNepU8d0/AsJCcGFCxea6jd9+nTHYzun14q85Q88lVNs67p16+KCBQuKfRO/Fd8jbkh4Oq4EBwfjqFGjuJwyu0ZEt2O4Hbs2Qx97e6M+i4O31yiM2la0XTvjSnHrzIl4V7YG88knnxj6NGavwcHB2KxZM9NYi5VrZY3Nro5WxlzZuoAnfVNfnrt1ek+QxYtm60Vm7VwScYoM2nzzE9hEat++fYbpS5YswZCQELdGFBISgl999ZWjsk6dOlUjR1xcHHbp0gXr1q2LiqJgs2bNcM6cOfjOO+/goEGDpB24bNmyWK1aNaxatSqGhYXx3ytUqIC9e/fGmjVravQLDg7GOXPmGMrlyebb4sWLMTw8XFqf4eHh2K9fP+zatStWq1aNL2azfO666y5L9TV06FBUFAVHjx7tkrZr1y4MDQ3l+TZt2hSfeOIJnDFjBr799ts4duxYrFatGk+vWbOm6R06Tk/ozHBKv7Vr12J0dLSliXZQUBC2a9eO/190pOzcWbNm8SDfbEDW/z8iIgKbN2+OvXv3xuTkZJ5eoUIFj5+CcQdrP0VRsF69eqZy6hfqrB6KomieJAIAbN++vWmA0LJlS5w3bx5eunTJUF6jumbtILvOHZ70TXHSarVvFnfzjV07cOBAUxmrV6+Oly5dwttvv53/VqNGDWzVqhWmp6drZB05ciTP38zm2bkhISH49NNPY2FhIb9GZvNGaRERES5Bib793PUVWZBmZmfjxo1z+V3MKzo6Gg8ePIgTJkzQ1JmZPWdmZuK8efPwww8/NLUXo+v0dxYDAF533XV4//33a8Ycd4FmbGwsvv/++1J7YYukvvbJjFmzZvEJhOhje/fujffffz8+9thjOHnyZHzsscc0+ssCbTu2nZeX5+LT3E20Y2NjsX379tivX79i+96SGhu9jSebb+K/rE7Xrl1rq1ynfP1/tTxE1zha9Msvv/wyvvPOOzyOfvnll/Ghhx7CLl26YFxcnKaPPf3006Z5stj8oYceMs3TyEeqqmpJdzvludNBj2jze/bswQoVKpiOj6JPdrf5pqoqjho1ChVFwaFDh7qUpyhFT6SxspKSknDo0KE4ceJEHD9+PLZo0cLSYqGdQ1EUTQyjj7FFHbp37665ToyxzfKvXr06VqtWDQGKnigXx4bc3Fw8evSoZj7Gxs2MjAxs3Lgx1q9fH8PDw3mZlStXxgcffFBqS07EFBMmTOBxmFE72ImZjGze2+OH0+ORU3MyX/T34uq3bds20/iC/a3fVK5evTr26NHDZVMgPDwcH374YRw/fjz269eP66GqKrZo0UJjUyJW5wlJSUkYHh5uyWcoioIxMTGavPRzHdk41qZNGxdfJ15nFCc3aNAABw8e7JHujOzsbKxcubKh3opStFa1fft2vPvuu136/tixY7Fp06b895iYGDx+/LihfuLCtxPjtC/WirzhD+zIaWRjsbGxOGLECNuxojvfExQUpCkzLCwM69Spg+3bt8e2bdtiuXLleFq5cuXw/vvvN+wPYWFhOGLECOzevbupXR89elQ6htuxaxlG9lnc+iwO3l6jMGpbMV6yM64Up86ciHeXL19uugajj1X0vxvFMcnJyS7jDzvcrbHZ1dHdmCtbF+jQoYPLvDE6Ohqjo6NdfEXHjh09Xqe3itGagXiYrRcNHjwY69SpYyirr+MUGbT55iewxjbafPv7778xPj6eG8WoUaNw27ZtmJOTg5cvX8bt27fzSaSiKFiuXDnpKzeKw48//sgDi7S0NFy4cCHm5eUhImL9+vVRVYvufmAwx2zk8IKCgnDz5s383OzsbK6Hqqr8jqGTJ0/i888/zztGREQE7t+/30U2q5tvv//+O8bGxqKiKJiSkoKzZs3CP//8E/Pz8/Hs2bO4ePFibNiwISqKgj179uR5nD9/Ht977z2sUqUKz2vSpElu66x27dqoqip+8sknLml9+/blQZH+aQ5GYWEhPvPMM7zMN9980/C8klpgdEK/CxcuYFJSkmYgHzdunOkEn13L/l+hQgUeCOgnX4qiYO3atXHJkiW4b98+XLp0KT8nKSnJJU9VVTEqKgpvvfVWHDx4MB+sX375Za/WoxjEiMHg0qVLuZxVqlThsgUHB2NQUBD+73//w6VLl2JaWhpPEwPcsWPHYv/+/VFRFOzXrx9fVFEUBV9++WVeVrt27TQbE+z3qKgovO2223DVqlWGCxjt2rXjE0h9nRldJ8PTvinWmdW+6Y3NN1HXli1b4sKFC/Hrr7/mwamqqvjcc89hWFgYNmvWDA8cOKDJZ+vWrXwhSlVV3Lt3r9Tmmc7s/N69e+PVq1c17SA+MaTvH2XLljUMFAEA09LSMCwsTJO/rK+IdhYWFobBwcH47rvv4p49e6R29t577/H8MzIyeNqQIUOwRYsWqKoqjh49GsuXL48hISE4d+5cjX5t27bVLI7odUlMTMT33nuP28sPP/yAoaGhPL127dp48uRJRHTvy3/77TeXxb4uXbpgr169sFy5cpq6uv/++03tpSQ33/bt28f7ZVxcHL7wwgtcfxlr167l+jE/Ehoaij179rRt22Lbt2jRgk+CxPaLi4sz9L1paWk4adIkvOuuu/jvnvre/+Lmm5E/YPX55JNPevQKTyd8/X+5PKM4etasWZZsNC8vDz/++GP+dH5wcDBu3rxZGptb0V3/9IE73e2WJ9PBCHHMFW06OTkZBwwYYOqTrWy+uZuzMB/4/PPP47Vr1/g5YpzCFvIVRcE5c+bgrl27NIv7DRs25GPSvn37NH6OjUu9evXCxMREl7GZjUn6GFvUgb3CPyQkxCXG1relPhYGAGzVqhUOGzaMn/fmm29i165deXqZMmXwo48+cmlbo/mazJaciCn0MRMrr3nz5pZiJrG/i7LqbT7QNt+cmJP5qr8XV7/rr7+et2X16tU18yfWL9u3b49RUVHclt58802+CM02q4zmeWZrFHqszBP0fTEjIwPXrVuH+fn5/Br9jWKsHyckJGCZMmX4b8xPyMYxsb/rfbl+Lq0f/9LS0vCxxx7DAQMGuNWd8corr/Bz+/Xrh3v27MGcnBxNn+7fvz/GxsZiVFQUjh8/3qVPzJgxg/82ZcoUU/2Y7N4ep321VlRcf2BXTlZ/ZnZmJ1aU9c22bdtq5mtGPsTI97C/xfUZJif7u0+fPi5yin1aP4YzrPZpKxjF3vp+5Gl92oXN47y5RmHUtvp4ydNxxW6dORHviv5EUVzXYETbS0pKwu3bt3Ofxn7Xb1KJNpqWluayhuHrGFu2LuBuzUC2Fm91nd4q+jUDcRyXrReJdcZibRYv2q0zJ6DNNz+BGZnR5tsLL7zA02fMmGGahxiomG3QFBfmuKtXr45nz57VpLH3rW7ZsoX/Nm/ePI0jioqKwq1bt2K1atVQVVW87bbb8Pfff9cc7GmcNm3a4KZNm/jBHi9nA62YtmnTJpw0aRLvaPo8xU2GcePG8cm72XuGr169is2aNUNVVfG1115zSevTpw8qStGdHvpBSw97XHjbtm2a3/Pz8zEqKgpVVcVp06ZZrvu2bdsappfUAqMT+k2fPp3ron88m/0u3lHCAk/9BEL2bcHU1FScMGEC7tu3j1/LBuDOnTtjq1atDIMBdsdv8+bNvVqP+iCmQYMGLq9t6Ny5s0aH8ePH8zQxsGUbHIqiYIcOHRARNU8GskCnQ4cOGt9z4cIFbNq0qUs9Mt31dSZe9+6775rWmXidDFnfZH350KFDfCLRu3dvTZ8/dOgQr6OQkBBcs2aN1BcUt42qVq3KF3QYom9ITU01fX/0n3/+iWXKlEFVVXH8+PFSm0dEXLZsGb8jjU1As7OzNe1w4MABHDlypKG9s3pliw76tCpVqmDVqlU1/cior+jtTLRBRHM769Kli8aub7nlFlQUBVu1aoXXrl3D6667jr9zXMxTb2f6u5rM5GSbNRUrVuSvLrDqy2fOnMnLjYyMREVR8O6770bEIr/21ltvYZkyZfg5Q4YMMZzYleTmm6i/J990GDZsGLftffv28SdN7r77bk3fqlixIu7du9elf/3++++4ZcsW7pPvvvtubNu2LSpK0aLrsWPHNH1Q73ubN2+uuWOa+RBVVfmCU+vWrT2qi5LefBsxYoTHx4ABA1wO5puNYhx2MD2//fZbXLNmDXbo0MFwU5NNyt999123TxI64ev/y+UZxdGe2uiZM2c0cbQsNpch6n706FHNnecy3e2WJ9MB0bWvtG7d2sXP161bF4cNG8bPGTZsGLZo0YLfbKEoCtdDtvnmbs6iqio+9thjLnLr45Ru3bqhoijYpUsXTRp7nRMbd8RxhW0kBQUF4YEDBzTjilj+zz//7BJjMx3EsfqBBx7QyJifn8/THnnkEbxw4YLmdUh6n8A2Jho3bswXY+Pj4922rai7zJaciCn0MZMYUyBai5kuXLiAkydPlto8e7pQ5nc9OYoTg1rBiTmZU/3dDjL92A1DSUlJLvMndkMVa1u2ANumTRsei7P+ftNNN2nmTyKizRthZZ4gPnmUlJSkmScw29i5cyfWqlXLsL+Ki7pBQUG4ZcsW6Timv571v7179/K0kSNH4oEDB3DChAluYzBWL2bxS8WKFRGg6EZYWbqiKNi4cWNs3bq1YZ8Q+7SRftWrVzesG2+M0+7WikaMGIG33347t6tmzZppdLz99tuxcuXK3OZuvvlmw7ow091bciIar2mJdtaxY0dTH+hJrGilbyYmJrr1IaLvEdt906ZNmjFS3/ZMznPnzrn0aRnu+rQV9HNxWT+yWp92EedxRmsUzCYjIyPx1ltvNbTLQYMG8Ziqfv36/Cbdnj17GsZndsYVu3XmRLwri1EQix4wEXVl8QTLV7zW7G9FUTRrML6OsWXrAlbXDI4cOaJZF7Ca5gn69SIR2XqRvs5k47gZ3opTZNDmm58gOgQ97dq1Q1VV8aabbnKbT+fOnVFRFOzevXux5DELqKKjo1FRFMzMzHRJY7vNvXr1Mp04sw9Mz5kzBxVF4TvMooMVg0q7hz4/trDHAm6jhVg9q1evRkVRMCMjwyXtypUrvHOOGzdOmk9ERASqqoo//fST5vesrCzumAcNGuR2MY4tfLLH7b0dwNnFnX6qquKJEyfc5rNy5UpUFIV/7JUtautheW7atIk7VlVVNQOjOKiJNmE24ImDpKqq/H3YR48excmTJ/NNCX2eTZpaf+oTAABlxUlEQVQ0wRYtWuAtt9xia4HVqP2YDEZ3YLG7fPVyIiK/+5ulvfbaa6goRa9pQ0RctGgR1/n//u//eJre93z11VeoKArGx8cb6q6vsx9++EEjo1mdsetkdcY2NPSTmBEjRhi2qVmf1wfkZn6iuG300ksvubTRd999x8t99tlnpTY/ZswYPrGU2TxikU++5ZZbMCUlhcvPJn2KomDfvn1xxIgRPLA2CyjNfmd1FxoaaviEGUsPDg42tUGZnbH3pDO7ZnaWmJiIiKi5c17MU2+fn376KSqKggkJCdKFYjZRmDZtmse+nPmVm266id+dV61aNRc7Z5vAqqpijx49XCY5Jbn5VrVqVb4o6Qk1a9bU2Laov9EijtlhZGviNexvsU+zti8sLMQ1a9ZgtWrVTL8PkZ6ejh07dsThw4db7rcl0Q6I6LaujA47MY6R7xN99bBhwzSvmWPp4eHh2L9/f1y6dKnhJrJZfGrF17/55pseTxwDqTw7479RHG3HRsU4Oi0tDVVV5XeA2tWd5ZmcnCzVnb1ZwOxGP6t1kZmZiQBFN+bpx3krY72V/iKWJ46P7uYsqqrisWPHXHTTzyGWLl3KxzkxTT/uiOPK8uXLeTniHOLo0aOa12lXrVrVJcY22nxbsWKFRsasrCyetm7dOkT8N8aOj4/XvB3BrK6nTJni1n5E3WW2JN4R7q2YQh8z6WMKOzHTddddZ3rTnpVxz+rh5HjkxJzMrn/RI/osu8j0Y+30+uuvu1zHYkJmZ+xpY/ZmIQDAAQMG4IgRI/hNwOHh4S4+i21UGKVZnSeIT3vq5wliP1q9ejXXid10II7hLI3FSapa9HpekaNHjxr2d32c9tlnn/FrWAxmFDOw89kTbFbjEDv++sYbb0QA0Dz1yvqt3l97Oy5wt1akj7HcjU0y31Ecf2B3TUu0MzYPUhQFBw4caDtWtNI3X331Vbc6ib7HaFwBACxfvrxp/TJ7UVXV0s2H4jhmF6M4UtaPrNSnXfTzOJHvvvvOa/M4ve17Oq7YrbPIyEhUVRVnz57tUb3IYpT09HTTGKVJkyb8dZTivL9p06Z47tw5l3ksy2PNmjWGdcf+X7t2baxYsaKtGNsq4pgrWxfwZM3AbF3EXZpV9OtFeszWi/RxClsv8rRfeyNOkUGbb36C2eQfEfkA88EHH7jN5/3330dFKdqx94Y8ngQR7hwzAOCPP/6IiIjbt29HRVE0rw5wN+H25DDKk8nF3pu+detWaR1cvnyZX3P69GmXdHbXZf369aX5ZGRkoKqq+Pnnn2t+/+233zTO2sohC/CcntDZ1U9VVcMP4erZsWMHt4ly5cqhqqr8o5oiYl8pKCjA0aNHawY0VVXx/fffx9GjR2NsbKyLbQQFBWFaWhpfADOyt08//dRF5u+//94lT719W21Hd4GOqqqGjzuzj9myc0QZQ0NDNWns2wdhYWGI+O+3EFRV5f2PTUZF37Nr1y5UlKIngfS6i6+/ZTqHhIRg79698fPPPzetM/E6WZ1Z8S9W+7z4u9k1xW2jVatWubTRqVOnuPxs0csMMeCX2bxo91YCYk98ZVRUFFauXFnznQKWFh0djfXq1eNPMVnpK2Z2xmyX2bXezjZv3mzoL/T2uXPnTp4n8+UzZ86U2mePHj088uUs8Fu8eDFu3boVFaXo9WJ6Ll++zO+YVFUV27dvr3l/e0luvrFJsP4OVHewOmW2Leqv71fuDnf9VByzjHyaeI67vm+l35ZEO+j1sHrYjXFkdY2ImJOTgx988AF27tzZ5dskqlr0MfUxY8bgjh07XOSX3UVu5utV9d+PlRuNEbL6CoTy7IwnZnbrqY2KcTT73sv27dstX2+ku5inTHd347+VPmkU19qN72Xnu4slzOYsZrGrfg5hNCZt3brVZQ4hjissDaBoAUbk8uXLWLt2bS4Ha1sWYxttvn366aeaPH777Teetnv3bkTUxtiVK1dGVS26U90sVg4ODnbbj0TdZbbExv93333X1JY8jSn0MZM+prATM8nOt+qDrR5OjUdOzMns+hc9Rv7FU2T6sbbZuHGjy3XM5lk7s9hG/ESBmR+Q+Sw78wS2Ga0oiss8QexHop8oV64cImrHcCN/VrZsWZcxXN/fxQ1Hd75cHzOY+WsrcYgT/lrsS96MC9ytFRnFp+7iMif8gd01Lb2/1s+D7MSKVvqmlVe5ib5HNq4wux47dqxGTtFeypUr5yKnHnEcs4u7ONJOfdpFP48TOXXqlEdjmLuxz128pEfWtlbrjJUZHx/vUZ3JbInFE3ofqY/127Rpg506deK/s2+TiWML+1cs84UXXjD0oapa9ESx2VqLbI3NCqKOsnUBT9YMZOsisjSr6NeL9JitF+njFLv92htxigzafPMTZA6ILaq7G1gRvWP0ojwyJ2w1Tfx93rx5iPjvonPFihX5hx+rV6+Ow4cP569JioiI4HnExsZitWrV+KsGVVXFatWqaY7y5cvzcoYPH645MjMzuU7stRueBCpGd85s2LABFaXorjkZAwYMQFVV8Y477tD8zl4BwvTxZBAsiQmdXf1kA6vIwoULUVEUrFSpEoaEhKCqqrhr1y6X84z6CvvoJquf1atXI2LRIG8W3CtK0Uc7xbtdxPpNSEjA++67z6X89evXI0DRJp7+XenemJgz+ZgOIuzOG3aO+G1H9mFhlrZmzRpUlKJH3BERv/nmG153q1at4mn6+vz+++9RUYpe2ann6tWr+Mknn2CPHj0MJ0tmdSZeJ6szq/7F6BD7O/tmSWhoKA4fPpzn5+022rlzp4uuhYWFvE737t3rki7y008/8QFeZvOIrj5ZVmdmabLJLvPH7HUw+rTKlStrFgTN+oqZnTHbZXattzN2V6bervX2+eOPP/I89b7czD5F3Y0+zK335WzM3bVrF79Ljy186Ll27RreeuutXM4WLVrwV4mU5OYbm0isXLnSo+tY0MtsW9RffAqtd+/eLuOsePTs2ZP7SfZkQ+fOnV3GY/2kxajtPfED/jQ2Mtjr7W666Sb87bffLB0sLsrIyMD+/ftj//79sUmTJqYxDjtYHfTp08elrvWcOHECp02bhvXr13epJ1Utepr8pZdeksanevS+Xp+n2RghEkjl2bFD2Xme2KgYR1esWBFVVcWlS5davt5IdzFPme7uxn+jvmslrpX1bVVVcfny5Yb9xd346M6HGKWrqopHjx51qQf9uLNkyRJUlH9f68zS9HMIcVwRF9VjY2Ndyli7dq2LjGzsEjff2ELOzTffrLlefO3kkiVLEFEbY3fv3h1VVcWHHnoIERE/+OADBPj3CROrsZ2ou8yW9OO/mS15ElPoYyZ9TGEnZvI07gwLC8NatWpJx0LZfNQJnJiT2fUvemT+xSoy/Vi7GD0RwWye2TV7PWRqaip/vZqRvbjzWXbmCeI35/TzBLEfiX5CvCGS8fnnn/M4y6jfsjFc399Z/xO/72ilv8+dOxcBQPO0qlgXNWvWxMcff5wv/s+bN8/UXxenD7obN70RF7hbK2JxXadOnbhM69evd9H1k08+4X7eqC6K+xpau2taen8tW9OyGivK+iZrSytP1oi+Rzau6O2ayVm3bl1pf9AjjmN28SSOtFqfdtHP40QKCwt5vbRq1Uo6D1m2bBkf51hdDxw4UGO7Yh17Oq7YrTNP2lZEZkv6eMIs1md5sE/QsDFF9PnsX6Myv/jiC5exRoxVjNZaPPFbekQdZesCnqwZyNZF3K2ZWEEfL+oxWy/SxyniepEneCNOkUGbb36CzAGxJ9+MnKgedkeBp4amx2yxqHXr1qiqKmZmZuKxY8c0aWyRJiIiAjds2KAJKpgDqVGjBhYUFGCHDh1QVVXs0qUL/x7PDTfcgIhFr2ZRVRW7devmksZec9CmTRsXmWXfzBDTatSogaqqGr6WQoRtXqiqavjdJraj7m6jc/HixagoRR+11QdH4mKZbGHuyJEjeN1116GiKNitWzdHAji7uNNPUYpedyUjPz8fmzdvjqqq4oABAzAuLg5V1fjJIbO+8sILL3A7S0pKwsOHD2sWv9krFcQPtIsDnlFwb1SXr7/+OipK0bu0T58+7fINLXat1UVWffuxgfbpp592KbtmzZqac7777juexoI4lnb//fdzORERH330Ua7TnXfeydP09fncc8+hoihYq1YtaZux+qtdu7Z0EqRHVmdVqlRBVVXxqaeecqmfWrVqoaqq2LhxY/zoo49cJrgi+r4p+jMWLHqjjdasWWNaN1YCSrb5FhMTI7V5vQ5Mnscee4zXX/ny5XHdunW4atUqzeRUVYu+/2Skg6IULXLp2499J4VtzoppZhtwrA3M7Iy9BoPZtd7Onn76aX6daNf6uhT7n8yXt2jRAhVFcfk+gKIoLufq7YUFft9//710zBG55557uKzXXXcdnjlzpkQ339hYPWDAAI+uY09nMNsW9Rdfh+aJbevbXhyP9f1YbHu9zT/11FMIAFi5cmVctmwZjhgxgr9GTOZ7S2psZHTt2hUVRcH09HTL1+hjH0Rr3wXT9xer3xLbsWMHPvTQQ/z1cfrx0eoEWeT06dP46quvYqNGjTwaIwKpPDvjv1Ec7amN6uNo9hTEjTfeqPlouztE3fV5ymDf4WKv6NPXtZUN519//RVbtmzJ/ctvv/3G801NTTUcc7///ntDefTliYtCiqJgnTp1cMeOHZrx0d2cRVVVfPTRR13K0o874pxFTNPPIcRxhaUBFN1oqIeNSfpXQK9atUozrrDX4xktvrLrevfu7RJjz507l495Bw8e1KQxn8oWnGT9SNRdZkv6MUDEbkyhj5n0MYWdmMld3GcWF3mC098gdWJOZte/iHjiX2TI9GNPcF533XUu1zGbZ+3I5hQDBgzAm2++WTMXF2MNvT2wbxW1b9/e9jyhRo0avDz9uCP2I9FPREdHu+Qjxq7MHo1ierG/628oYP2hbNmybu1a7O87duzgr8/U+4n69eub+k/Eoqcs2HWzZs0ynZeNGzcOAUAzVzWaz8j6kt24wN1aEYvr2Gvj7K4VFdcf2F3T0sc9Vte0ZLGirG+ym+eTkpKkPkTve2Tjisyu2Tftxb5hxa7tYjeOlNWnXfTzOD2s76WkpEjzEedxRm2r/76lp+OK3Tpj83vxO7+exvp6W5KtwbA3F1i5QUBV5ZtvYpwia3t3MbYV9DrK1gU8WTOQrYtYXTORIYsXEc3Xi/RxirheZBVvxSkyaPPNT2BGtH//fpc0Zkxff/2123y+/fZbVJSinfPiYLZYxBa9VVXFPn36aD7ump+fzx+/zcjIwG3btmkcMzvi4+O5Y/r4449x1KhRfIAcMWIEz3/RokWaNLaZoKpFry7RY3Xzjb2mMCUlBU+ePGmof25uLjZv3hwVRXF5JQzj66+/NqwjI9idjgkJCZrH8U+ePMkHj+joaMNXoh05ckTzMXOzuxKcntDJkOnH7qa48847LevXsGFD02BSNliLQUBSUhJ+/PHH/Pw77rgDFUXBuLg4/OKLL3D8+PGYkpLCFxcBgL+DunLlypicnOxSl//88w/fIBo8eDAi/ttXUlJSNHl6ij6IiYuLc3l3f9++fTV32LRv354HsUOGDNH0M/ZI/+DBg3HFihUYHh7Ov23FXrMyePBgTX0eP36cb/YPHz5cKq943b59+zzS3azOZH1T9AXNmjVDRdHebS2i75uiPytOPxHbSFVVXLBggeF5aWlpmJ6ejocPH5bmt2LFClSUondjy2xer4OIzOaN+opehz179uCyZctw4MCB/LUDYl956623NGl6OxP7iszOxDxXr16tsbM9e/ZgXFwcn8SKdi3qoO9/Mnt55513+LUJCQkIAPxpIj16e2HfUGjfvr10zNEzfvx4fn7t2rX5HWAl4ZNF/R944AG8cuWKpevYps+cOXPwgQce0Og/atQobmsHDx6U5iPattj2e/bscdl8E32v2PaizRv5XsSiJw/19qunJMdGRMTJkyfz8o3GQCNEf8c+OO3k5hvDqD7tTpBFPBkjAqk8O+O/URztSTsdP34c+/Tpw8//+OOPpbG5DHbN2rVrXfK0qkPHjh3xnnvu0ejubsPZSAdE176iH69mzpxpmJ++PH08ZeSTZXMWRVH4EwXPPvus5tU2bNxJTk7GIUOGaOYs4pjEFnvZHIKNK2+//TafXwCAYR2JY9L48eP5eBsUFKSpo5MnT/INurCwMM03uUT9U1NTeR2uXLlSoztbCGdpr7zyCi+vatWqePfdd7vY9T///MP9OtNdZkv6MUDEbkwhxkxGsaudmMkIvS1FRkbi3r17bcf7vhiPvD0ns+tfGGb93dv6ibbUr18/jX47duzgC6PiIurKlSvx8OHD/OmxMmXK8NfnibGGkc0bYWWeMHr0aN6f2ec4GCz/Xbt2afyE0Td0RD/BrjOK6Y18nVgeq4uHHnoIH330UUv9HVHrr3v16sXLY+s14eHh+M0332hkXrFihaYdrr/+epw5c6ZLn9D3adl8xmpf8iQucLdWxHRnethdKyquP7C7pqWvT0/WtBDNY2+zvvnOO+/wukpLS3MZBxCNfY9sXJHZ9eHDh/kYfvvtt2P//v1d5ghW+7QVihu3WpnLWIXN48zWKOLi4rh/ks1JxHkcomvbivGSnXHFbp2J41GLFi2wZ8+elupMZkvu1mCYTwsLC8P+/ftzH/J///d/mo030Z+KZcrW2Iza3l2M7Q6jMVe2LmBlzeDKlSsu6wJW0jxFFi/K1ovEOuvRowd/La04jsvwdpxiBm2++QmsoWXHU0895TafV155BRVFwaZNmxZLHtlikXiHY2hoKHbr1g2nTZuGS5YswYULF2KZMmVQUYru2KxWrRp3SuKdTgCAkZGR2KNHD2zWrJkmEAYA7NChA+7evRvvvPNOlzsNmjdvzhejRKxuvu3Zs4fXaWpqKs6ZMwdPnDiBBQUFeP78efziiy/w+uuv5+e//PLLhnU0ceJEVBQFO3bs6LY+//jjD8zIyNAsAowfPx4/+OAD7NKlC9cxJCQEmzVrhv369cM+ffpggwYNMCgoiDt02YZISS4wyvR77bXX+GASGhpqST+2Cdu1a1eXsmSDNbMRlqf4HQr2+LGq/vuR23Hjxmk+2Dxx4kRs2rQpqmrRHQ9vvPEG5ubmYk5ODq5YsYI/faiq/z7WrO8rhYWF+O2333pch2IQo5dz/Pjx2L9/f5e7fNjCTYcOHTRpYj9LTU3l9v7II49oPnw/btw4XtbkyZM1rwFw970yo3awqrtZncn6JvNtYpAzaNAgQ5vX902xvDfeeMN2P9FPqidNmuRxHiIvvPACKkrRHUIym9frIPpkmc3LJqtMBzHtwoUL+N577xn2lYceeggbN25saGcVK1bkQY6ZnbHFOdF2AQDbtm3L39P9+uuva/qf+F7vGTNmuPQ/mb2cOXNGM/4AALZs2RKfeOIJ3L17N547dw5zc3MREfniZrNmzXDRokVYvXp1zbhjNuYY8cwzz3AZ2VhYEj752rVr2LRpU15+YmIi3nvvvbho0SIX/XNzc/HcuXO4e/duvOGGG1BRFM3GC9N/xowZmoUhGaJt6+1JfH2ime/dvHkzTpo0iV8nvlff7NVXzH71lPTm27Jly3j5Vm6iQkTDuvbF5psIq8/iLiqIWBkjAqk8u+O/Po5mT5UqioI//fQTHjx4EI8dO4YHDx7En376CZcsWYLTpk3Dbt268dcXKoqCPXr0MM1TjM3N8mS+mL1yR5+nJzp069YNR40ahUuWLOFPKLHYy6oO+r6i3/jo37+/pXYQ7V7mk3fv3s2fFAkODuavNFIUBWfOnMn9YPny5XHgwIE4duxYHDp0qIv/ateuHRYUFODGjRtRVbXf9Zs8eTIWFBTwO4KZDEwvozmEPoZ59NFHXcoEAPzggw/wqaee0oyp4eHh/Mka/ZGYmMjjb3GcAyi6C/7tt9/G77//HuvVq6dJS0pKwvr162PPnj2xTZs2/OkvNq66syWjmMIorvUkprj55ptRURSsW7euYexqJ2Yywig2Zz7ZTrzvi/HI23MyRHv+xV1/97Z++r6gKEUbrGzRUt8fwsLCcNy4cfjdd9+5vIIRALBPnz44ZswY7NWrF3+VoqIUPR1nhpV5gix2ZTrVqlVL4yeMniQQ/YTROCbGRHpfd9NNN+Hbb7/Ny2CvqmO+rn///m511/trVt7PP//Mb3ZTVRWbNGmCAwcOxCZNmvB2EP16pUqV+N/79u3Dd99916VPy+YznvYlK/3W3VrRpEmTNHZmd62ouP7A7pqWvj49WdPSI9qZzPfUqVNH07/i4uKwXr162Lx5c36DBbOLnj17WopRzOz6+++/dxnD+/bti507d/a4T1vBm3Gr2VzGKoMHD5auUVidk4jzOETXtmVPMyqKYmtcKU6d6cejzp07Y79+/WzHu+7WYIx8Wtu2bV3ms+L4w16x6ckam9j2shjbzpgrWxfYsWOHxk8kJibiHXfcgS+++CK++OKLeMcdd2BCQgL3340bN8adO3fiokWL8N577+VvGvB0zcQIWbwoWy/Kzc3lG7ximwwbNqxE4hQzaPPNTxADe7OjYcOGbvPp1KkTqmrREyTFQeaYr1y5wp+YYeeIh97o7RxmeWVmZuLZs2cNZba6+YaonZSZHcyBsAVKkcLCQn5n3LPPPmupTrOysrBNmzYu9abX0UgORVHw/vvvlzqzkl5gNNNPr4sV/T788EMeSP3555+acmSDNUt77bXX+ERCrBPxrgzxYHW/Y8cO/Oqrr9zahRic2llYNUJsPzM5FUXB8uXL8zRP+5U4kWO2JqaxMoYMGeJW3uIETbI6M+ub+n7SvHlznDVrlovNG/VNsbyHH37Ydj/RL8K0b9/e4zxEWrVqhaqq4pgxY6Q2r9dBrDOZzcsmq6z9zdpPZoMpKSkutuTOzozsVZSzSZMmeOXKFZf+Z3Su2P9kvtydb5WVIdqZ2Zhjxmuvvebi80qCs2fP8jsV3Y13RnWmKIpmzGWvpVFV8ydQGKJtI2rtychujHyvXha7CwMlPTayj9srioJTpkyxdI1RXW/cuJF/N8gMfZ/3hu7eXFQobeXZHf/1cbSRv5T1UTZOi3enuovNZb6P9TF9np7oIOu7VnXQ9xX9mBseHm7oj/XtoO8rMp+8e/durFGjhmk7GOlnNmeRpen9Htv00c8hzOYXU6ZMsZy3u7mVkayysdNKnu5sydsxhdH4IMaudmImI4w239yNfzJ8NR55c06GaM+/uOvvTuhnd93BXX9h5XTv3h0vXbpkKpfVeYJZ7Kq36ejoaFQUhcdSDL2fsDKOib7OrDyZTeh1l8U2bOHeaGxo3Lgx/11cCDc6l/Vp2XzGqb5kdX6RkpJie63ISlxXHDlFecU1LbE+7axpybC63iVbq/AkRjGyayP7tWrXdvB13Cpj+vTpUt9jdU6in8churatWb+1Mq4Up868He+6W4NBNPdpLE/2fUlx/DBaX7CyxmZXR3djrmxdwE7cpy9Ptk7vCbJ40Wy9yEyHkoxTjKDNNz9hypQplo6LFy+a5nHkyBG+Ky++esQOVhzzmjVrsFu3bnwCKR7FCXaN8ilXrhyOHTtWuvl06dIl/h5wK2nPP/88fzWf0dGzZ088f/68YVknT57El19+GV9++WU8duyYR3X7wQcf8PcVG9WVKENkZCTefPPNbj+ki+idAM4b6PWTHWb6Xbx4kdvVPffco0mTDdZi2sqVK/nduWJg/txzz2F8fLxm4GHOmeX52muvYVBQkKHMHTt2xL///pvnZ2dh1Qh9+xnJeeutt+KpU6c0aXb6Vr169fiETj9AjRw50nASIatrT3FXZ0Z9U5SzRYsWeP78eUObN+qbYnmPP/64qZ9wByuvR48e2KpVK7zxxhtt393Dvs+pqkVP8shsXq+DWGcymzdqI6aD3uaNkNngRx99xJ9GsmJnRu2oz5Mh9j/9+fr+hyj35QCADRs25N9LtHKI5U2YMMFW+86ZM4d/lN6phQErFBQU4OzZs7FOnToe6R4VFYWzZs3S2Pe1a9dw6tSpOGXKFFy1apVpmXrbZnjqe/V2ZdT2VvCXsdETrNa1Hn2fl8VFdvN0mkAqr7jjP4uj2dM/+hhQf4SFhWG3bt003zkwy9MoNjfr761bt5bmaUUHsTxZXGtFBwbru4MGDeLzm4kTJ7qcZ6UdZD756tWr+Mwzz2D58uUttYOiFL12ki22GNWrOJnX10lGRgY+/fTThnMI2fxizpw5Lgs6ZmWY1T87WPy9ceNGfOaZZzA1NdVyfrKyZbbkzZhCf44+drUTM8ls8Pbbb7flk/V4wyd7gjfmZCKe+BdP+7u39DOz2YiICG7z3bp188i+GzVqhJ988olbeTyZJ7ib67Rp04b7Ev0T/3o/YXUcY76O9Xd3fsIT3fWIfVpRil5XN2nSJPztt9/4bwMHDnSZJzBdxD5tpJ8vYruSWityWk6xPp2S08z3eHtcQXS1ayuHXbs2wmr/8wXbt2/3+hqFHm+MK96oM2/Fu+7WYBhGPo356JkzZ2q+A623dU/W2OzqaGXMla0LWF1H1B916tTBOXPmFOuJNz2yeJFhtl4LAJiQkOA3cYqIgogIRKng559/hp9++gkAALp37w6JiYk+KffSpUuwbds22L9/P/z555+QnZ0NV69ehfDwcIiOjoa8vDy4fPky5OTkQHZ2NuTk5EB4eDgAABQUFAAAQHBwMBQWFvLrYmJiIDU1FerUqQOZmZlQvXp1R2Q/fvw4LFy4ELZv3w7nzp2D6OhoqF27NvTq1QuaN2/uSJmMrKws2LJlC+zfvx/Onz/P6yUmJgYqVaoEdevWhSZNmkBkZKSjcjhFcfVbsWIFnDt3DkJCQmDgwIH89/Xr1wMAQLNmzSAiIkJzjT5ty5YtsGLFCgAAeOqpp/h5165dgwMHDkBeXh5Ur14ddu7c6ZLn3r17Ye7cuXDgwAEIDg6GjIwM6NmzJ3Tq1MlLNeQevZyxsbEuafv374cNGzbAr7/+qpGzRYsWsGvXLvjiiy8MdTh9+jQsXLgQ1qxZA0FBQdC8eXPo06cP1KxZ05JssnbwBiXZN30B85kAAIMHD4awsDBTm5cxf/58AADo27cvxMbGamw+PT1dkyZitf1kNggAkJ2dbcnOxLQuXbpAYWGhaZ4A//a/H374AYKCguC6666Dvn37mvY/K/Zy+PBh2Lhxo+FYpR9z1q9fD6dOnYKoqCgYM2aM+4YwYMmSJfDll18CAMDcuXNt5eFNPNG/OGOukW0z9Pb0xRdfAIDWRv3B9wYqen/gDZz29f+18oyQxdFi32zatClER0d7Lc/8/HyoVasWtG3btti6O6GDyKxZsxz3yYWFhbBp0ybYvHmzNHZt06YNpKenS8ed1NRUl7Tc3FyoVKkS1KpVq9g65OTkwMCBAw3ljIqKgqNHj8K1a9cgNjYWcnNz3cbfMt3Dw8MhKCgIQkNDITo6GlRVtW1L3owpjh8/DjExMdC0aVPL9Wl1nlDa8Pac0+n+7il6/bKzs+Hy5csQHh4O9evXhyZNmrjot3v3bnjuuedg9+7dcPnyZQgODoYyZcpASkqKYX93Ar0POXfuHCQnJ8PDDz8MMTExprGUHk/HMdbf33rrLTh+/DjExsZCXl6eqa8rDtnZ2ZCXlwcJCQmG6UbzhO7du2vmoyU5TgfKfNQTOZ2IFc0w8z1hYWFw7do1UBQFgoKC4OrVqy4+JCsrCzIyMmDIkCGW5PR0DPcWvqxPXyCbx4kUZ1zxZp15I971ZA1G9GmiHgcOHIAVK1bApUuXoFKlSrbX2Ozq6OmYK1sXAABN/wQAn6/Tu4sXAeRrBv4WpwAA0OYbQRAEQRAEQRAEQRAEQRAEQRAEQXgJtaQFIAiCIAiCIAiCIAiCIAiCIAiCIIjSAm2+EQRBEARBEARBEARBEARBEARBEISXCC5pAQg5f/75J+zfvx/++OMPyM7OhitXrkBMTAzEx8dD3bp1oUGDBvw9rKVNTrM82Xt68/Ly4Pjx43Dp0iW4cuUKREREQHR0NFSqVAlq164NTZo0gaioqBLVwZ/K8zX+ZBPR0dEQEhICERERkJeXZ1mW0tBGdnTIycmB7du3w4EDByArK8tyH7N7nQxZnqGhofy7kUFBQbxti+sL7OJLm3fCBovTfv4ip6c2cfXqVVi9ejVs27YNzpw5A8HBwZCSkgKtW7eGG264wSvylgbstu9ff/0Ff/zxB0RHR0O1atWk3ywh3OMvY5ITvt4d/uJjnNDPqfLs1pmvrwt0fB2nFKc8O/FwScQGvrwuUGKf4uBtOUtiDAgEWdwha4fExERARDh+/HipayOAwLdBf6vPQJHT1+t5hDGB3v/c4cTY7y5eKlu2LBw8ePA/E28E2pqJv9koAAAg4Xf8+eefOG7cOKxSpQqqqio9IiMjcejQobhp06aAkfPSpUv4xRdf4Msvv4yvvvoqrlixAnNzc3me/fv3x/DwcAQATR6KoiAAaA5FUUzLjIiIwJ49e+KGDRu8roOv6yxQcEI/WZ4ymzBKY4dMlm+//RYbNGiAISEhbm3NiTZSVRVr166NL7zwAmZlZVlOs1pnMh3Wr1+PPXr0wIiICLfXiX3sjTfesHWdrG/KZPGkba2WZxdf27wTNrh+/Xps3rw5qqrqYvPM7sX6ZfW5ePHiEpXTqk0Y9d2IiAisV68exsXFmcrcsGFD/PHHH03l2bNnD44ePRpr1qyJ0dHRGB0djTVq1MA777wTd+zYYVtPf8FsPGZ2IdZxeHi4pn337NmDrVq10tRnVFQUDh8+HE+ePGlLnhUrVmD79u2xQ4cOHqUFOiURN5jVp90xwq7vLQlf6Ev9nCjPbp35+jon8LVPtjsmydp27ty5pjq89957tuIiu/GwFfu0EhsYxRT+YIN2+5+vYx+7ONE3fe0jPZFFZmfeksVOrGF3HlucNjLzS8VpIzsxWGmwQX+yeX+T0067O7GeZ1dOd2m+xtuylIb+504/s3Vju2O/u3hJny6zXSfiKW+0oZ6zZ8/ikiVLcMmSJXjt2jXDc958801MTEx0kZMd7tZMrKKq1tY6ZfiTjeqhzTc/4+WXX8bIyEjeucUAUvy/Pk1VVRwxYgT+888/fi2noigYGhrqYvQVKlTAvn37YmRkpMapGS3yySaXZnUzdOhQvsFXUnUdKG1rFyf0M8vTqk14YjPiYOLpdd5sIzHPoKAg7NixI37wwQeYk5MjTStOO7D6tHKeWRtYvcZd38zNzcVbb71VEwTZaXNPfIFdfGnzTvgJsa7t+FmzevaVnMWxCatpoaGh+NVXX7nINGnSJAwKCnKxU7F/TpgwwbKO/gazQ319y+qc6d+vXz8sW7asoS2oqoqVK1fGY8eOeSzTvHnzeB6epAUyJRU36OtT5pft+np/0t3X+jlVXnFi8+DgYJ9d52kbTZ06FadOnYpnzpwxPceXPtmJMUmfj1n8Y3aNN+Nhlq+7tjSTy914azRu+NIGPdFPdq2TNl9cvO0/S2IMMMNMFiv2XVxZPI01ZO1QnLmMuzaS+Qy7beRpDBboNuhPNu+vcnrS7u76qMxeney3/jR/8KYsgd7/rOqn93XFGfu9HUdZKdOofKNrvdGGZrzyyiuoqirWrFnTMP2+++7TtLuZvmZrJp4g6mC21mmGv9mooX6IiED4BY8//jhMmzYNWJNUrVoVKlSoABcvXoRffvkFCgoKICwsDMaPHw9RUVFw+PBh+Omnn2DXrl0AAKAoCjRp0gRWrVoFcXFxfifnzp07NfkoisL/Fs1QURT+/3r16sG+ffv4/1VVhcLCQgAAyMjIgGPHjvFr6tWrB2+++Sb8/fff8Msvv8D69eth9erVUFBQAIqiQPfu3WHp0qXF0sFuXQdK29rFCf1keeptolq1ahAXFwdnz57lNsFISUmBmJgY+OWXX1zkrl27NrRq1crQPlVVhcjISCgoKIArV65o0jp37gyVK1d2pI1UVdX0AdZPoqKiIDs7G1RVNUzr168f5ObmwqJFi4rVNxVFgY4dO0LHjh2hVq1aUKlSJYiKioKwsDDIzc2FnJwcyMrKgl9++QVeeeUVOHPmDL+2XLly8Mgjj7i9TtY3e/bsCcuXLwdEhKCgILjxxhuhbdu2UKtWLXjllVfghx9+4GnNmjWDKlWqAADA0aNHYevWrTzPzMxMeOSRR9yWZxdf27wTfkKsa1VVef2wvES/zP5u3LgxnDhxAv766y+eFhUVBdddd53P5fz8889NbWLt2rVw/PhxrodoE4sXL4YFCxZoymjRogXMnz8fCgoKoG7dugDw73gTExMDhw4dggoVKgAAwNSpU2Hq1Kn82tjYWKhRowYoigKHDh2Cixcvcj2nTp0KTzzxhFsd/Qm9HbJ/69atC/v27dOcq7cTkdTUVOjWrRuUK1cOjh8/Dt988w2cPn0aAABatWoF33//vUdyzZ8/H0aMGAGKovDXqllJC1RKMm7Q16fML9v19f6ku6/1c6I8b8XmVapUgaSkJMeus9NGLC7au3cv1KlTxyXd1z7ZzpjExhw2rujjlNmzZ8ORI0d4GcHBwXD99deDoiiwY8cOuHbtGk+rXr063HHHHW7jIjYOMsLDwyE9PR3+/vtvOH36tMZ3m8XDsphw4cKF8Pjjj4OiKPDcc8/Be++9B7/++iu/lsUGP/zwg6b+WJwRHBwMMTExcP78eU260zZoN+Y108/f5nJO+E9f+0gZZrIwW9TPkcLCwqBOnTqgqirs3LmzWLJ4EmtYnccCACQkJEBwcDBcvHgRrl69yn9PTk6Gu+66C7KysjxqI71f8kYbeRKDlQYb9Ceb91c5PWl3K2s3nq7n2ZXTapqv8ZYspaH/eaKfuG5sd+xfunQpzJ8/n+eVmpoKUVFRhvESQFGcWVBQADk5OZo8x40bBwkJCR7HG1bjKSfijR49esA333wDEydOhP/973+atC+++AL69evHZWjdujV88803oCgK7Nu3D44ePcrXVQoKClzWTDxFtg7ar18/uO2226BDhw6G1/qTjZripU08opj8+OOPfMe1c+fOePjwYU366dOncfTo0agoClarVg0vXrzI044cOYJDhgzhu7Z9+vTxOzl//fVXDA0N1dwd0KJFC/z8889x4MCBmt8TEhIQoOhR1o8//liz85+cnMzTrOielZWFPXv25OmLFi3yeV0HStvaxQn9ZHnqbSI9PZ3nya4T7xRhebIyevToobljY9GiRfjJJ5+43L3Su3dvXuZff/2F7du352nBwcH4+++/S3WwC8tn3Lhx2Lx5c8M7YmJiYjA5Odn0jpkqVargqlWrLLWDvj7FOpPBrlPVoidXWb9ctGiRJT2N+qaYZ69evfDPP/80LE+fJsvTSpqn+Nrm7eYpQ1+fr776quZuKr0Nin2G/R0ZGenS7r6UU2YTVatWRVVVcf78+S7tfsMNN6CiKJiUlIQdOnRwsQn2/3nz5vG7BidOnIiIiIcOHeJPV5QpUwYXLFigeUXDtWvXcMGCBRgfH4+KomBISAj3FYGA3g6nTZvG6+ODDz5w8b1//fUXzpo1iz/RnpaWxu2jV69emrxzcnJw4MCBPL+VK1d6JFug3LnqDUo6bhDr04rvleGp7y1pX+i0fk6UZ7fOZDGTE9ch2msjdu6+fftc0nztk+2OSez3NWvWuLSfqAN75TmLT8UYjaUxHWTliTZRrlw5w7H61ltvdYmHPYkJRT8hK4+d06BBA9M7u8XDSRu0G/NaqU9/mMs54T997SNlWOljRvMnVicpKSlYvXp1j+csDKuxhqwd9LZbuXJlTTts2bIFq1atyvtD9+7deZrVNhL9khWstJHVGKw02KA/2bw/y2m13WVrN4j21vPsyulJmq/xhiylof95op84T5XpJxv73dXZ8uXLXWIVpsORI0ewb9++/PeoqChb8YbVeEqmI5PH03iDrZl8/fXXLmlszSQ1NRUPHz5saqPr1q1zWTOxg5VxvHLlyjhp0iQ8ePAgv86fbFQGbb75CcOGDUNFUbBp06aYn59veh57lPLBBx90SXvhhRe4wXz77bc+l3P+/Pn8aNmyJSqKgp06dcL58+djt27dUFGKHkft2LEjd0Djx4/HzMxMVBSFL+SKDqpLly6oKAp27NgRb7nlFlTVf99fblX3goICbN++Pc/P13UdKG1rFyf0k+VpZBMsT/G6Z599VpOnuHhzyy238EGyS5cuWKdOHW5z9957r2ld33TTTfy6J598UqqDXfSLTAcPHsTHH39cs6jNzlEUBZOTk7Fs2bKagIClN2/eHN9++208f/68aTuI9fn8889b1kG87tq1a5o+ZhV93xTzLCwsNC1PnybL02qaJ/ja5mV5in53wIABGt965513mh6pqan8hoZRo0Zh27ZtXRbEnnvuOU2++kMMRGvXri21M5mcYpr+qF+/PiqKgnXq1MF58+bhnXfeyetNZhPR0dGoqiru3LlT0+433ngjX+RcuHChoU2IfXDSpEmoKAped911iIj48MMPo6IoGBYWhtu2bTNtpy1btmBYWBiqquriK3yBrE5lBxuPMzIycM6cOZr67tWrFyqKgpmZmS7ty+pFVVVuW8y2xfxnzZqF5cuXR0VRsG3btlIb1R+ijerlFuUsDTjhDzyxA6v9zCqe+F5fx0y+1s+J8uzWmSxmcuI6EU/aSB8Xidj1yXZ9pN0xSdRB336iDlu2bDGNizZv3qzRQVae2Ea5ubmaPEV9WrRowcfxevXqafTr37+/1IeIusvKE3W/9dZbUVEUbNKkiSau1R8PPvigNKawa4N2Y16ZfrLyREp6nm5XTl/7SBlW+xii+fxJjF2TkpLw7bfftjU2ypC1g5g2ZMgQ0zG8cuXKXE59/9OP7zK/5E4fdowaNQqTkpIQADAlJcV2DFYabNCfbF6GXTnFtps7dy7Wrl2bjwF2Y0W7azci+naXyelP8we78YQTsgRK/3NinirTTzb2u6szli6ut7E+rffJAICNGjVyiRe9FU/ZbUNZnYaHh6OiKPj0009rfp85cyZfb7z33nvd2qh+zcQOsnGcjd9sE05c62Q3U/u7z6bNNz8hIyMDVVXFjz76SHrenj17UFEUjI+PNzSsNm3a8E7razn1nYE5IPFv/f/FAFgfELOn4FRVxaVLl3Ld2bme6L506VJUFAUrVqzo87oOlLa1ixP6yfKsWLGii02wPPXXiXmKzly0pQoVKvCnMtu3b+9ynV4HZpuZmZlSHewiW2RifSQqKsplAGJyhYaGuqSFh4fjzTffjEuWLMGdO3dq6kysT0900F8n9jFPEK/T5ykrz2qenqRZxdc2L8vTzO/qfamdQ8yb/Z2UlISKomBERISLz5bZmUxO8Xf9IZNFZhOxsbGoqir/+C9r98TERH49u1NMbxNiH9y4cSMqioKxsbGIiHj99dejqqp45513urWTkSNHoqIo2Lp1a7fneht39WqnvitVqoSqquLs2bNd7JC1t6qqOGPGDEPfK+ZXXNs0kr24E1Z/wgl/4KktWOlnnmDV9/o6ZvK1fk6UZ7fOZDGTE9fpsdpGsrjIrk92wkfK2lavg9h+eh1kcZGog6w8fRuJeZr5CaPx350PYb9bKU+Mv5lN6GMKsTxZTGHXBu3GvDL9ZOXpKcl5ul05fe0jZXjSx/Rp7mIPT8dGGbJ2ENOKM4abraXo5ZT5OidisNJgg/5k8zLsyimzJ7uxot21Gz2y+UNxbNbJ+YPdeMIJWQKl/zkRg8n0k4397uqMpbMNLbZ+iKhte9l6tzt/LutL3og3rIwDsvm6u/pGRJc1EzvIxvG1a9fiiBEjMDY21nQddNKkSZq3X3iK0z5bBcIvOHnyJAAA1KpVS3oeS7948aLmPf6MO+64AxDR5f363sKKnFi0qav5vz5d+f/vb5WdBwBw7tw5ACh657m7upHpnpycDAAAFy5c8HldB0rb2sUJ/WR5XrhwAQC0NsHy1F9nVmdivhcuXIC8vDwAAOjVq5fb65jtHjhwQKqDUyiKAhs2bIAPP/wQunTpovkGHABAXl4eREdHQ+XKlfk7k3Nzc+HLL7+Evn37QqdOnQAReZ2J9emJDvrrxD7mCeJ1+jxl5VnN05M0q/ja5q3kyfyukW+1C8tTzOvChQugKAqsWbMGPvzwQ2jZsiVPk9mZTE7Z4U4WAGObSElJAQDg33pk52RnZ/NzwsLCNGlGNsHOYe/e/+233wCg6P3o7ujZsycAABw8eNDtuU7hrn49qe+zZ88CQNG3A/R2mJ6ezs9r0KCBoR/R5+dtvUoTTvgDu7bgie+VYdX3+jpm8rV+TpRnt848jZmKe50eb8RMxfXJ3vSRduMUvQ6yuEjUQVaevo2M7FPmO636EE/KE9PFGFSMKerXr8/Lk8UUdm3QbsxrVz89/jBPF9OtyOlrHymjOLIoisIPIwoLC/lhxQfIkLWDmOZuDNf/XzzM1lLM5PRm3OSJLzDDn23Qn2xeRnHldDe2eTIm2l270SObP3gj1ndy/mAn1va2LIHW/7wZg8n0k4397uqMpbdv357/pu/Tok9m/9fL6nQ8pcddX7LS943SzNCvmXibdu3awZw5c+DkyZOm66DPPfccJCUlwZgxY+Cnn37yuAynfXawI7kSHsM+/icuDBohposf32awTsg6rbeRyRkREQFXr16F5s2bw9ChQ+H+++8HRVHgpZdegsceewwKCwthypQpkJaWBkePHoVnnnkGwsLCICgoCK5cuQITJkyAmjVrwsaNG2HWrFk838LCQvj4448hIyNDKptM97179wIA8I+N+rKuA6Vt7eKEfrI8K1SoAFlZWfDzzz9rbOLatWsu15nVmZhvYmIiH7BiYmIsX2ckm6/aKDw8HIYMGQJDhgyBkydPwoIFC2DChAl8sLt06RLk5OQAIkKFChWgQoUKsGfPHgD4dwG9sLAQOnbsqKnPxo0bW9ZBf53YxzxBf52Yp6w8T/K0mmYVX9u8LE/R7959990a34qIkJ+fD1WqVNEEjAAACxcuhJycHGjdujXUqFEDDh06BBs2bNAEjv/73/8gJSUFNm7cCLNnz9bonp+fD0OGDIHc3FzYtGkTlClTBtLS0kztTCbnjBkzTPUdO3YsnD9/HkaOHAmZmZkaWWQ20b59e/jll1/go48+guHDh/N2r1ixIuTl5cGpU6dgy5Yt0KZNG6lNbNmyBQD+3cy7fPkyAABUrlxZ2k7iORcvXnR7rrfR17dV7r33XpfxmNU3s42YmBgXOyxTpgz/f2xsrMa29bL88ccfMHnyZAgKCoKgoCBTG9Uj2ujcuXM1aaKcpQEn/IGsn+mx2s88warv9XXM5Gv9nCjPblxrJ2YqznV6vBEz2fXJdn2k3TFJj9h+TH8mnywuEnWoWLGiaXn6NhLzPHPmjMt8DQCgTJkyEBQUpNFP5kOMYgOj8v744w9+jZktsZiiSpUq0KJFCwgNDYXatWubxhR2bdBuzCvTT1aenpKcp4t4IqevfaQMu7Lo+/v3338Ps2fPhsjISEhISICsrCx+Lot1kpOT4dlnn9XkYzXWkLWDmCYbw8W+GRoaCjNnztTkI/bN6OhoU78k83WjR4/WxGAszoqKioIhQ4ZozrUag5UGG/Qnm5fh7f6QkJAA06dPt5yP1XaXrd3okc0fRDmzs7P9Zv5gN57Q4w1ZAqX/iXGIt+apIp6M/e7qjKWfPn2a/5aYmAgA2rbPzMyEl19+GQAAXnrpJXj++ee9Hk/JdNQj60si77//Pqxduxbq1KkD48aN06SNGTMGLl686La+AVzXTJwiIiLCZR308ccfh/z8fEBEOHv2LLz55pvw5ptvQp06dXj9WcFpnw1I+AWNGzdGVVXxsccek573wQcfoKIUfXT7ypUrLunbtm1DRVEwLi7O53K2atUKFUXB+vXru8iZmJiIqqriihUrXOTU58k+5KgoCgYHB/NHXe+66y7++KuiuL520kz38+fPY7Vq1VBVVRw6dKjP6zpQ2tYuTugny3Po0KGoKApWr14d3333XU2e+uvEPMXHmN99913+ePKQIUP4aydvv/12l+uMdAAAw0eqvdFG7l6bYpbGdG/SpAl/nF98LHzXrl348MMPY1xcnOYRcrE+z58/b1kH8bpff/1V08esou+belnMytOnyfK0muYJvrZ5WZ6i39XnqU8T0den+AFdvZ2JaaKcRvVpZmcyOWXI5JTZxNatW/mrCF599VWNnFOmTEFFUbBly5Z44sQJFx1Y/uvWrcOUlBRUVRXvu+8+RERMS0tDVVWl3xZilKTPlrW9DLPxWFVVrvvy5csNbVu0G5kdbtmyBRVFwYSEBI/kDJQPpnsDJ/yBJ1jtZ1bxxPf6OmbytX5OlGe3zmQxkxPX6bHaRrLYx65Ptusj7Y5Jog769hN1cBcXiTrIypON1aLu+njYk5jQamwg6u6pLZnFFHZt0G7M6y72MStPT0nO0+3K6WsfKcNqH9Mj2rwsdjWaP4lYjTVk7SCmycZwT+acMr8k83Xu6sWq7ma+QIY/26A/2bwMu3J60u4yrLa7bO1Gj9k4LRvHPJHTkzSr2I0nnJAlUPqfE/NUmX6ysd9dnbH0Ll26aOIlRG3bT506lad7Gm94stZipqMeqzGvuGYyc+ZMTZq4ZnLlyhXT+j5x4oTLmokdZOO4DFbXZcqU4d+W97Qv+cJn0+abn8A+UBgREYFr1641POfIkSOYnJyMqqoafm8KEXHRokWoKArWqFHD53KyD4YHBwfz9+oyOW+44QZUVRWnTp3qIqc+T7FTz5o1y/R9ze50z8/Px0WLFmFGRgYqioJBQUH4448/+ryuA6Vt7eKEfrI8N23ahEFBQaiqKgYFBaGiFC3sG8ki5slsin2clNnS22+/jampqQhQ9L00/XVGOgAAVq1aVaqDXWSDjixN1H3NmjW4fPlyHDRoEEZGRhrqkJiYiJGRkZr6TE9PxzFjxiAAuNWBXcf6POuX7BtbMsz6pl6WTz75hH/4VpYmy9Ndmh18bfOyPEW/m5OTo8lTnyair897772Xt6HezkSfzOQMDQ3lixT6+jSyM5mcMmRyurOJO+64gy9ciPaZl5eHLVq04HrobZcFbPHx8dwvvP/++7h+/Xps1aoVqqqKixYtksqN+O97w418hdPI2l6GbDxu1qwZqqqKzz//vKFtDxs2DIcPH44nTpyQ2uHnn3+OilL0AWpP5Pwvbb454Q88QaxPd/1Mhh3f6+uYydf6OVGe3TqTxUxOXKfHahsxW5w3bx6uX79ec9j1yXZ9pN0xielw7733YsWKFfk1b7/9NtdhzJgxbuOi8ePH8xhUVp5srGa6s8UWNkYalSeLCa3GBuycb775pti2xGIKuzZoN+b1NPbxx3m6XTl97SNlWOljRnMkZvNBQUGYlpZmKktBQYHh/IlhNdaQtQNLCw8P59+2NxrDmZ2Z2aesjUS/JPN1VuvFne5GviCQbdCfbN4JOT1pdxlW2122dqPHaP5gJKc/zR/sxhNOyBIo/c+JeapMP9nY767OJk6cqFlPYPESorbty5QpgwCAERERbsvUU5y1Fitt6K6+R40axct58MEH8cSJE4iImJeXhy1btkRVVbFhw4b40EMPadbiz507h7Nnz8bU1FQ+ru3fv99KUxoiG8dl6Ot64sSJOGDAAMNxXI8vfTZtvvkJp06dwri4OFRVFUNCQvD222/Hjz76CNesWYOff/45PvTQQ/zjgqqq4uLFiw3zufPOO1FVVezXr5/P5XzkkUdcNsiYnKzDN2rUyEVOfZ6tWrXidzZ+/vnnWL9+fcMNuIcffhinT5+OM2bMwLlz52JmZiYqioLVqlXDrl27YpkyZfhCsqIoOGXKlBKp60BpW7s4oZ+7PJs3b66xhaioKOzatSveddddGB4ezh1n+fLlEQAwOTnZ0IaYTGxAVZSiu0eqV6+OilL0QXu9DuzcNm3aSHWwi2zQkaWZ1dlXX31l2g7//PMPIiI+9dRTLnVRoUIFlz42Y8YMnD59Oj788MPYtWtXDAsL09RneHg4du3a1e11Zn1TL4uqqlimTBmeZ+fOnfk1LEhKT0/H9PR03u5Mls6dO1sqzw4lYfNmeX788cf8tw0bNmjy1KfpMWp3sW889dRTOHfuXBw+fDj/vUOHDi79qF+/flI5//nnH6mc7jCT051N6O1TUYo+oJuamso33cS0hIQEHjgb+QnxePDBBy3LbeQrnMZd25shG487derEJwOe2LZelgkTJqCiKNi1a1eP5Pwvbb454Q88QV+fMr9s19d7W3c9dn2M0/o5UZ7dOtNfJ4t9vHGd3TYy8sHF9cl2faSYjydjklEMynyr/v+yuIidHx8fLy2vUqVKLmXVr18f+/XrZyjPwIEDpeUZxYRWYwMmW1RUlFds6Z9//imWDdqJee3EPkaU5Dy9OHL62kfKMJNFH7uKsjRs2NCln7mThc2RRKzGGrJ2mDNnjksMOmbMGMO1FFZvRvYiayPRr3Tr1o3nM3HiRNv1YjUGKy026E8272057fQHIzxpd7O1G9l6nkxOf5o/FCee8LYsgdL/nJin2h379euHLVq0wFtvvRX79euH119/PYaEhGjsMC0tzXT9GwCwRYsWbsu0G0/ZbUN39Z2bm4tdu3bl5wQHB2OzZs1w5MiReP/992NERITL2MLalv2uqkU3lBUHlo+nm2+IxjbaqVMnv/LZtPnmRyxZskSzqKU/mEGMHj3a8PpLly5hfHw8KopSbMO3I6d+AinKuWLFCn7++vXrXeTU5ylu4In/Fw99/RhtqChK0eOnM2bMKNG6DpS2tYsT+rnL02xxXG+HskNcsDBaCNHnqygKli1bFlVVxUcffdStDnaQDTruBqTitMO7776r2XiwstjF6kqsR3fXuOuboixGeVppX6NrZOXZoSRs3ijPI0eO8N+ff/55TZ5i2vTp0w3lEOtaVo+i33W3MeWpnFYwktNOn3fnB/R56dPYkZKSgteuXZPK3LBhQ0Nf4QustL0ZZuOx3jdatW29LNWrV0dVVfGpp57ySM7PPvsM09LSMCMjw6O0QMXb/sATjOpT5pft+npv6i5iZzz2pX5OlGe3zsTrZLGPt65jeNJGZn64OD65OD4S0bkxKS4uThoXFTfuNTpat27tUXlGsbgsNtD/7g82aCfmtRP7iJT0PL24cvraR8owksVd27D08PBw27J4EmvI2kFms6Kdsev19mKljdzNaz2tF09isNJig/5k896U06n+YHftxugc/ZxML6c/zR+KG094UxbEwOh/TsxTizP2exJHmfUlduj9tTfjKbttaKW+8/Ly8Mknn3RrO0ZrJBUqVLD0Bgp3sHLtbL4h+r/Pps03P2PDhg1Yr149w4ll2bJlpc4pOzsbt2/fjtu3b8fs7OwSkRMAMCYmBidOnKg5Pz8/HxMSElBRFLzxxhsN5XSn+9ixYzExMdHtoqiiFD1Se8MNN+CLL75o+m5iX9d1oLStXZzQz12ezzzzDL744ot4ww03YHBwsEugZnSEhYXhHXfcgevWrXM53njjDaxdu7Zpeffeey//v/4uE2+1kWzQsTIgFacd/vjjD3zwwQexQYMGmvp018d+/fVXw3aw2zcRi967bJanWdsWpzw7lITNG+W5fv16XLduHe7atcslT5Z2+PBhU1n0dS2rU1af99xzj7SveCqnFYpjE1btJTQ0FBs2bIgPPPAAfvPNN4Z+gh2XL182lXXdunWmvsJXWGl7M4zsUJwAv/TSS6bXGtk2k+Wtt97ifuyHH34otpylHW/7g+Ii64Pe9r0lETP5Uj8nyrNbZxs2bMA6der47DpEz9pI5oeL45OL63vsjElGR1BQENatWxfvuusuPH78uGl5X331leYau+WJ8fCYMWNMy7MTExrFBqy8uLg4v7JBb+nntF+yg1P+09c+UoansSvrY9u3b/e6LGbI2iEmJkbzjTmxv8TExODYsWNN28HTNjKrGyfrpbTYoD/ZvDflLKl2N1u7sSOnP80f/EkWxMDof96epxZ37JfFS5GRkThy5EjT9e/o6GgcPny46fjuD/GG1fr+9ddf8bHHHsOqVatK5QwNDcW2bdviG2+8gZcuXbLYcnIUpXibb4j+7bMVREQg/I6ffvoJtm/fDufOnYPo6GioXbs2ZGZmQkREREmLpsETOc+ePQu5ubmgKAokJyfbyvP8+fOwd+9euHbtGlSsWBH+/PNPyM7OhqtXr0J4eDjExMRAamoq1KhRA0JCQryugzcIlLa1ixP6WckzPz8fDh06pLGJ48ePw/Hjx0FRFEhPT4d69epZksWsvNzcXLh48SIAACQnJ1u2sZKguO1gVJ9W+pjd6+zKUqFCBQAAOHXqlNfK85SSsnkn8LT9/FFOdzZRtmxZOHz4MJw/fx5ycnJ4WqVKlaB69eoQHBxcbPkuXLgQML5Chrfb99y5c3Dp0iUAAKhcuTIoiuJNcUst/hg3OOHrjfBHH+PE2OLN8uzWma+v8yVO+2RfxCmiDuXKlYNjx45ZLs9uPGxFP2/GBiVlg4ES+3iKk3L62kcGiixGyNqhtLdRadLPH+ozUOS0u3bjj/UZyJSm/meEE2O/u3jpvxRvnD17Fvbv3+/omomT+IONitDmG0EQBEEQBEEQBEEQBEEQBEEQBEF4CbWkBSAIgiAIgiAIgiAIgiAIgiAIgiCI0oJ/PydImHLhwgVYsWIF/P777xAdHQ3169eH1q1b+90rnGRy+jrNCR2cIFDa1i6+bqNAsTN/0t1ueU5c54Tuvsaf/FKg9BV/0s+ptvAnfF3fvs6ztBMoftkJqDzf5elPdubr8gJlTCoNY3ygxBROXOdr/KmvOEGgzPP8qT/4ul4CJU9/Ks8u/uTP/MmufU1pl8Wf8iwJW/Inf26X0t6XSkwHx78qR3jEnj17sF+/fli+fHmMjY3FZs2a4ccff6w5Z/78+RgTE4OqqmqOatWq4XfffVficrK02NhYzcd9VVVFRVEwKioKIyMjvZbGdJ8wYYJH9eLrug6UtrWLE/qVBjtzoj7t6i7TgV0XHx/vUfvZvc6JdvenNnJCd1mesnbwp77ihJ050R9UVcVKlSphmzZtAtpn+9qe7La9P/Vrf8KJ9pNd91+OmUpDeb62FyfsrDTUma9jtEAZ40tD7Oprm7eLP/UVX8ffTsWE3vZZgeIn7NZLabBBf7J5J+QMlDl8oMwf/MleAqX/+VPc6s6WKlWqhDVr1kRFUVzS2ZqBP8Q3vu6f/mS//h6j0eabH7Fy5UoMDw/nhiMa+6RJkxARcdmyZRgcHMzT9Ud4eDiuWbOmxOQcMmQIT2Od1ulD7wCt1Iuv6zpQ2tYuTuhXGuzMifosju5mOhidZ6X97F7nVLv7Sxs5obvddvCnvuKEnTnRH/RlBqrPLgl7stP2/tSv/Qkn2s/XvilQYqbSUJ6v7cUJOysNdebrGC1QxvjSELv62ubt4k99xQndfT1HcsJnBZKfsFMvpcEG/cnmnZDTCX/mxBy+OHbt7T7tRDsEiiz+lKcTY3hJxFJOxDe+7p/+ZL+BEKPR5pufcO7cOSxXrhxv+Fq1amHTpk0xJCSEG8727duxWrVqqKoqDho0CD/66CP89ttvcd68edi9e3d+bVpaGl65csXnchp1zrJly2JQUJBhJ1ZVtVhpTIbMzEyeFhkZifPmzZPWi6/rOlDa1i5O6Fca7MyJ+rSru0yHxYsXY0xMjOa60NBQt+1n9zon2t2f2sgJ3e22gz/1FSfszIn+8O233+Jbb72FoaGhPC04OBgbN24cUD7b1/Zkt+39qV/7E060n699U6DETKWhPF/bixN2VhrqzNcxWqCM8b6ul9Jg83bxp75SGuZITvisQPETduulNNigP9m8E3IGyhw+UOYP/mQvgdL//CludWdLAOByXkZGBgYHBxv60Pj4+BKJb/7La2yBEqPR5puf8OKLL6KiKBgSEoJffvkl//2XX37BihUroqqq2KhRI1RVFRcsWGCYxyuvvMKNa/78+T6Xkxk8M1wmpz5NdD4vvPCC7TSxXli6ke76evF1XQdK29rFCf1Kg505UZ92dZfpIJZ3xx13cB301+nbz+51TrS7P7WRE7rbbQd/6itO2JkT/UEskwXOLC2QfLav7clu2/tTv/YnnGg/X/smWZ5O6P5fLs/X9uKEnZWGOvN1jBYoY3xpiF19bfN28ae+IsvTCf2cignt6OBPczlf10tpsEF/snkn5AyUOXygzB/8yV4Cpf/5U9zqzpZE/6hfF9Bv+CiK4pLmq/jGbn2Xhr4UKDEabb75Ce3atUNVLdqF1TN9+nRuCP369ZPmk5mZiaqq4oABA3wuZ9WqVblj0cupTxPltJumrxeZ7mKar+s6UNrWLk7oVxrszC5O6C7TQV+e7DqZnFavk9WZXd39qY2c0N1uO/hTX3HCzpzoD/oy9WmB4rN9bU92296f+rU/4UT7+do3yfJ0Qvf/cnm+thcn7ExGoNSZr2O0QBnjS0Ps6mubt4s/9RVZnnbx9RzJrg7+NJfzdb2UBhv0J5t3Qs5AmcMHyvzBn+wlUPqfP8Wt7myJpaWmprq0kXhtQkKCaZov4hu79V0a+lKgxGi0+eYnlC9fHlVVxU8//dQl7fDhw9xgFi1aJM3nnXfeQUVRsHr16j6XMyEhge+a6+XUp4ly2k3T14tMdzHN13UdKG1rFyf0Kw12ZhcndJfpoC9Pdp1MTqvXyerMru7+1EZO6G63HfyprzhhZ070B32Z+rRA8dm+tie7be9P/dqfcKL9fO2bZHk6oft/uTxf24sTdiYjUOrM1zFaoIzxpSF29bXN28Wf+oosT7v4eo5kVwd/msv5ul5Kgw36k807IWegzOEDZf7gT/YSKP3Pn+JWd7bENqYefvhhlzYSrx01apRpmi/iG7v1XRr6UqDEaMFA+AUXLlwAAIDU1FSXtJSUFP531apVpfnUr18fAABOnz7tPeEEZHJmZ2fzv/Vy6tOYTqdPn4YrV67YStPXi5imR6wXlqev6jpQ2tYuTugna6NAsTO7ONHHZDro61rUQdZ+ejmtXqfHG+3uT23khO5220Fv1yK+7itO2Jld/dzZi1hmUlKSJi1QfLZdG7Vb33bb3m6eevx1fLSLE+3na98ky1OGr2Om0lCeE+OHr+1MRqDUmRNjkl3/6U9jvK/rpTTYvF38qa/I8rSLr+dIerzhs3w9l3MqVhZx2l5Kexxil0CJFe3O4QNl/uBP9hIoY4A/xa1WbalBgwZQvXp1fh2A1rZbtGgBs2fPNkzzRXyj57+0xhYoMZrqSK6Ex0RERAAAgKq6NglL0/9tRExMDACAxol4E7ty6tNEOZ1I02N0na/qOlDa1i5O6Fca7MwuJaU7K092nUxOq9fpcbrdZeXZxR9t3m77sb+d7iu+trPi9GmxTLM0ozL1lKTP9kd78maeevx1fLSLr9tPz38pZioN5ZVUf/emnckoTXXG/vbV2Gm3vECR09cxha9s3i7+1FdkedrFiXmCE7GGr+UsiVhZxGl7KQ3jtBOUhlixNMwf/MleAqX/BUrcKv4/PDxcui5QtmxZ0zRf+HM9/6U1tkCJ0WjzzU8oV64cAAD89ddfxcrn0qVLAACQkJBQbJmMkMnJ0mTXMUQ57abpsZrm67oOlLa1ixP6lQY7s4sTusvk1Jdn167t6u6Ndpfl6UmaVXxt857IIrNro+tksniapsdpO3OiP+jLLI69lKTP9rU96bHa9v7Ur/0JJ9rPE/5LMVNpKM/X9uKEnXlSnl38dcz1RM5AHONLQ+zqa5u3iz/1FVmedvH1HMmTtJKUsyRiZauy2OW/FIfYJVBixdI+f/AnewmU/udPcatVWzp79qzpmgEAwOXLl03T3MnqjfjGkzxFSkNfCpQYjV476SekpKTAr7/+CllZWYbpc+fOBQDjRzBFjh49CgAAFSpU8K6A/x+ZnCkpKXD06FG49dZbXeTUp/30009czri4OFtpANp60aeJiPUSFxfn07oOlLa1ixP6ydooUOzMLk70MZkO+rrW62DWfno5rV6nxxvtLtNPVp5dfG3zsjxl7aC3a/11vuwrTtiZXf1kcurL/OeffzRpMln1lKTPtmujdutbn6fVtrebpx5/HR/t4kT7ya7T81+KmUpDeU6MH05cJ9NBRqDUmRNjkl3/6U9jvK/rxW77OXGdnpKcpwMExhggw9dzJLs6+NNczqlY2axenLCX0h6H2CVQYkW7c/hAmT/4k70EyhjgT3GrO1uqWbMmHDp0CK5evepSnmjbOTk5pmm+iG/0/JfW2AIlRqPNNz+hSZMm8P3338OOHTsM04cNG2Ypn40bNwIAQLVq1bwmm4hMTpYWEhICsbGx0jRRzrS0NFtpANp6kelulKev6jpQ2tYuTugna6NAsTO7ONHHZDro61p2nUxOq9fp8Ua7y/STlWcXX9u8LE9ZO+jt2ug6X/UVJ+zMrn4yOfVlhoSEaNJksuopSZ9t10bt1rc+T6ttbzdPPf46PtrFifaTXafnvxQzlYbynBg/nLhOpoOMQKkzJ8Yku/7Tn8Z4X9eL/jqj8vzd5u3iT31FlqddfD1HsquDP83lnIqVzerFCXsp7XGIXQIlVrQ7hw+U+YM/2UugjAH+FLe6s6UePXrAq6++Cvv374fDhw9ryhNtm21MGaX5Ir7R819aYwuUGC3YkVwJj2natCkgInz33Xe288jNzYXFixeDoijQqlUrL0r3LzI5rabp5UxKSrKVZlV3WZ528aSuA6Vt7eKEfrI2ChQ7s4sTfUwmp1Xd7ZYnw1vtLsvTapon+Nrmrcpitz590VecsDO7+snkFK9ds2YN5OTk2LKXkvbZdm3Ubn2L2LUnT/K0mhaoONF+VvmvxUyloTwnxg8nrpPpYLU8u/jzmGtVzkAd431dL3bbz4nrZDo4gT/1FVmedvH1HMmuDv40l3MqVrYii11K2gYDZe0mUGJFu3P4QJk/+JO9BMoY4E9xqztZZOsCVtJ8Fd9YzVNWN57kaTXNE/zJpzkBbb75Cd26dYNvv/0WAAAQERRF8TiPhQsXQkxMDMTExED37t29LSIAyOW0mvbxxx9r5KxYsaKtNKu669PEPH1R14HStnZxQj9ZGwWKndnFiT4mk9Oq7jI5PbnOqiye6C7L02qaJ/ja5q3KIrPrku4rTtiZXf1kcorXrlq1ChYtWmTLXkraZ9u1Ubv1LeJJ29vN02paoOJE+1nlvxYzlYbynBg/nLhOpoPV8vy5zpwYk+z6T38a431dL3bbz4nrZDo4gT/1FVmedvH1HMmuDv40l3MqVrYiS6DaYKCs3QRKrGh3Dh8o8wd/spdAGQP8KW51J4tsXcBKmjtZvRXfWM2zNK6xBUqMpiAiOpIzQRAEQRAEQRAEQRAEQRAEQRAEQfzHUEtaAIIgCIIgCIIgCIIgCIIgCIIgCIIoLdDmG0EQBEEQBEEQBEEQBEEQBEEQBEF4Cdp8IwiCIAiCIAiCIAiCIAiCIAiCIAgvQZtvBEEQBEEQBEEQBEEQBEEQBEEQBOElaPONIAiCIAiCIAiiFLFu3TpQFAUURYF169bZzmfKlCk8H4IgCIIgCIIgCMI6tPlGEARBEARBEAThJXJycuDdd9+Fbt26QUpKCoSHh0NYWBiUK1cOmjZtCnfccQe89957kJWVVdKiEjqGDx/ONxt/++03l/R58+bxdPEICQmBxMREqFq1Ktx4440wYcIE+Oabb6CwsND3ShAEQRAEQRAE4RcEl7QABEEQBEEQBEEQpYEff/wRBg8eDH/88YdL2tmzZ+Hs2bOwfft2mDt3LlSoUAFOnjxZAlKWHqZMmQJTp04FAABELDE5rl27BufOnYNz587Br7/+CmvWrIFp06ZB5cqVYcKECXDPPfeUmGwEQRAEQRAEQZQMtPlGEARBEARBEARRTA4dOgRdunSB7OxsAADo1asX9O/fH2rUqAGhoaFw9uxZ2L17N3z77bewdu3aEpbWGlOmTIEpU6aUtBh+yf/+9z/o3bs3//8///wD58+fhx07dsDKlSth06ZN8Mcff8C9994Ly5Ytg88++wwiIiJKUGKCIAiCIAiCIHwJbb4RBEEQBEEQBEEUk0mTJvGNt7lz58Lw4cNdzunUqRM8+uijcObMGfjkk098LCHhTVJSUqBevXouv/fo0QMmT54MmzZtgqFDh8KxY8dg+fLlMHz4cFi4cCF9P48gCIIgCIIg/iPQN98IgiAIgiAIgiCKQUFBAXz99dcAANCkSRPDjTeRcuXKwX333ecDyYiS4oYbboCtW7dCamoqAAB88skn8OWXX5asUARBEARBEARB+AzafCMIgiAIgiAIgigGZ86cgStXrgAAQLVq1YqVl6IooCiK29c9tmvXDhRFgXbt2rnNs7CwEN577z244YYboGzZshAVFQUNGzaE559/Hq5evWp63ZQpU7g8Mq5evQpvvfUWdOzYESpWrAihoaFQvnx5uPHGG2H27Nlw7do1tzLm5ubCzJkzoXv37pCSkgJhYWEQFRUFdevWhVGjRsHKlSv5d93mzZsHiqLw770B/Ftv4vHbb7+5LddJEhMTYcaMGfz/zz//fAlKQxAEQRAEQRCEL6HXThIEQRAEQRAEQRSD0NBQ/veBAwdKUBJX8vLyoHv37rBixQrN73v27IE9e/bAggULYM2aNVCxYkVb+e/evRt69+4Nv//+u+b3M2fOwJo1a2DNmjUwY8YM+Oqrr6BChQqGeezatQtuvvlmOHbsmIvs+/fvh/3798Ps2bPh2LFjkJ6ebkvOkqJr165Qs2ZNOHjwIGzbtg1OnDgBycnJJS0WQRAEQRAEQRAOQ0++EQRBEARBEARBFIOyZctCWloaABRtRk2bNg0KCwtLWKoinnjiCVixYgV07twZvvjiC9i+fTt88cUX0KlTJwAA2L9/P/Ts2RMKCgo8zvvIkSPQtm1b+P333yE2NhYmTpzIy1i5ciXcd999EBwcDNu2bYPevXtDfn6+Sx4HDhyA1q1b8423vn37wqJFi2Dbtm2wefNmeP/992Ho0KEQFRXFr+nTpw/s3bsX7rnnHv7b3r17XY6UlBSPdfI2iqJAx44d+f+///77EpSGIAiCIAiCIAhfQU++EQRBEARBEARBFJMHHngAHn30UQAAmDBhArz77rvQq1cvuOGGG6BZs2aQkZFRInJt27YNRo8erXn9YePGjaFPnz4watQomD17Nmzfvh1mzJgB9957r0d5Dxs2DC5evAjXX389rFq1ChITEzXpnTt3hh49ekD37t1hy5YtMG/ePLjzzjs15wwdOhQuXboEqqrChx9+CIMHD9akN2/eHG677TY4d+4cREZGAgBAmTJloEyZMlC+fHl+Xr169TyS3Zc0atSI/33o0KESlIQgCIIgCIIgCF9BT74RBEEQBEEQBEEUk4cffhjuuOMO/v/ffvsN3njjDRg8eDBUqVIFKlasCIMHD4avvvqKf7vMF1SoUAFeffVVw7TXXnsNypUrBwAAb7/9tkf5fv/997Bp0yYAAJg/f77Lxhvjpptugv79+wNA0bfaRFatWgU7duwAAIAHH3zQZeNNJCEhASIiIjyS0V9ISEjgf//9998lKAlBEARBEARBEL6CNt8IgiAIgiAIgiCKiaqqMHv2bFi1ahXcdNNNEBysfcnIqVOnYNGiRdCrVy9o1qwZHD161CdyDRw4kD8xpic6OhoGDhwIAAD79u2DkydPWs536dKlAABQs2ZNqF+/vvTcNm3aAEDRU3jXrl3jvy9btoz/PWbMGMtlBxrR0dH87+zs7BKUhCAIgiAIgiAIX0GbbwRBEARBEARBEF6iU6dO8M0338C5c+dg+fLlMHXqVOjZsyfExcXxc7Zv3w6tW7eGv/76y3F5mjZtKk1v1qwZ/3vv3r2W892+fTsAABw8eBAURZEe999/PwAA5Ofnw/nz53keO3fuBACAypUr82/mlUbEDbfY2NgSlIQgCIIgCIIgCF9Bm28EQRAEQRAEQRBeJjY2Frp27QqTJ0+GpUuXwqlTp2DOnDkQHx8PAAB//fUXPPnkk47LIX4XzYgKFSrwv8WNMXecPn3aljyXL1/mf589exYAAJKSkmzlFSgwPQEAypYtW4KSEARBEARBEAThK4Ldn0IQBEEQBEEQBEEUh7CwMBgxYgQkJyfDTTfdBAAAn3/+OcycORNU1bl7IhVFcSTfgoICAABo2LAhLFiwwPJ1KSkpjsjjz7An/ACKXtNJEARBEARBEETphzbfCIIgCIIgCIIgfESXLl2gUqVKkJWVBX///TecO3cOypUrx9MVRQFEhMLCQmk+OTk5lso7deqU5XRPnspKSEgAAIBLly5BvXr1LF8nkpiYCADgk9dvlhSICKtXr+b/z8zMLEFpCIIgCIIgCILwFfTaSYIgCIIgCIIgCB+SnJzM/9Y/mRYTEwMAAH///bfp9YgIR44csVTWtm3bLKd7sol2/fXXAwDAr7/+CidPnrR8nUijRo0AAOCPP/6A33//3ePrnXqqz5ssX74cDh8+DAAALVq0gIoVK5awRARBEARBEARB+ALafCMIgiAIgiAIgvARly9fhv379wNA0Xfh2BNkjIyMDAAA2L59u2ke33zzDVy4cMFSeZ9++ilcuXLFMC0nJwc++eQTAACoU6eOR99e69WrFwAUbQS+/vrrlq8T6dmzJ//71Vdf9fj68PBw/ndubq4tGZzk7NmzcPfdd/P/T5w4sQSlIQiCIAiCIAjCl9DmG0EQBEEQBEEQRDG4dOkSNG/eHJYtWyZ9XWRhYSE88MADkJ2dDQBFG1j6p7fatm0LAABbtmyBH374wSWPkydPwgMPPGBZtpMnT8LYsWMN0x555BE4ffo0AADcc889lvMEAOjcuTM0a9YMAABeeuklvolnxt69e+Grr77S/HbjjTdC48aNAQDgzTffhIULF5pef+7cOZdNRHGz8OjRox7J7zSbNm2CZs2awZ9//gkAAEOGDOEblgRBEARBEARBlH7om28EQRAEQRAEQRDFZOvWrdCzZ09ISUmBPn36QMuWLSEtLQ1iYmLgwoULsHPnTpgzZw7s3bsXAADi4uLgmWeeccln9OjR8Pbbb8O1a9egZ8+eMHnyZMjMzIS8vDz44Ycf4JVXXoH8/HyoXr06f52hjCZNmsA777wDx44dg7vvvpt/b+6dd96BlStXAkDRKyTFJ7Ss8tFHH0GzZs3g/PnzMGjQIFiwYAEMGjQIqlevDkFBQXD69GnYuXMnfPXVV7B582YYO3as5mk3AIAPPvgAmjVrBpcuXYIhQ4bAp59+CoMHD4YqVapAQUEBHDlyBFatWgWfffYZ/Pzzz5Cens6vveGGG/jfDz/8MEyaNAmSkpL4hmZ6ejoEBzsz5T1+/Dj8/PPP/P/Z2dlw7tw52LlzJ6xYsQI2bdrE03r06AFz5sxxRA6CIAiCIAiCIPwTBRGxpIUgCIIgCIIgCIIIVK5evQoZGRmWv31WvXp1+Pjjj/lTX3peffVVeOSRRwzTypYtC19++SU8+eSTsH79emjbti2sW7dOc866deugffv2AACwcuVKmD59Oqxatcowv1q1asGaNWs036FjTJkyBaZOnQoARa+XNOLQoUPQr18/zUaUGVOnToXJkye7/P7TTz9B3759ISsrS3r9sWPHNJtvAACDBg0yferO6HwZw4cPh/nz55teO2/ePBgxYoTl/NLS0mDixIlw1113Wb6GIAiCIAiCIIjSAT35RhAEQRAEQRAEUQzCw8Ph+PHjsHnzZli9ejVs3rwZDh48CKdOnYKrV69CVFQUJCcnQ8OGDaF3797Qr18/CA0NNc3v4Ycfhjp16sCrr74KW7duhcuXL0NycjJ069YNxo8fD5UrV7YsW2hoKCxfvhxmzpwJ77//Pvzyyy+Ql5cHVatWhUGDBsEjjzwCERERtnWvUaMG7Nq1Cz755BNYvHgxbNu2Dc6cOQMFBQWQkJAANWvWhMzMTOjbty80atTIMI/GjRvDwYMHYdasWfDll1/Czz//DOfPn4fw8HDIyMiAli1bwqBBgww30hYsWABNmjSBzz77DA4ePAjZ2dnSV386QXBwMMTExEBcXBxUqVIFmjZtCu3atYPOnTuDqtKXHgiCIAiCIAjivwg9+UYQBEEQBEEQBEG4MHnyZHjmmWcgKCgIrl27VtLiEARBEARBEARBBAx0Gx5BEARBEARBEAThwj///AMARd+nIwiCIAiCIAiCIKxDm28EQRAEQRAEQRCEC7t27QKAoldLEgRBEARBEARBENahb74RBEEQBEEQBEEQAABw/PhxOHbsGCxduhTWr18PAABdu3YtYakIgiAIgiAIgiACC/rmG0EQBEEQBEEQBAEAAMOHD4f58+fz/1euXBl2794NZcqUKTmhCIIgCIIgCIIgAgx67SRBEARBEARBEATBCQ4OhtTUVBg1ahRs2rSJNt4IgiAIgiAIgiA8hJ58IwiCIAiCIAiCIAiCIAiCIAiCIAgvQU++EQRBEARBEARBEARBEARBEARBEISXoM03giAIgiAIgiAIgiAIgiAIgiAIgvAStPlGEARBEARBEARBEARBEARBEARBEF6CNt8IgiAIgiAIgiAIgiAIgiAIgiAIwkvQ5htBEARBEARBEARBEARBEARBEARBeAnafCMIgiAIgiAIgiAIgiAIgiAIgiAIL0GbbwRBEARBEARBEARBEARBEARBEAThJWjzjSAIgiAIgiAIgiAIgiAIgiAIgiC8BG2+EQRBEARBEARBEARBEARBEARBEISX+H/5l6nKJ6vLXQAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1800x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Count the occurrences of each label\n",
    "label_counts = balanced_df['Labels'].value_counts()\n",
    "# label_with_min_samples = label_counts.idxmin()\n",
    "min_samples_count = label_counts.min()\n",
    "mean_samples_count = label_counts.mean()\n",
    "max_samples_count = label_counts.max()\n",
    "plt.figure(figsize=(18, 10))\n",
    "# plt.rcParams['font.size']=20\n",
    "label_counts.plot(kind='bar', )\n",
    "plt.xlabel('Subject ID', fontsize =20)\n",
    "plt.ylabel('Number of Samples (ECG Beats)', fontsize =20)\n",
    "plt.title('Sample Distribution of '+f'{filename}'+' After Balancing Procedure', fontsize =22, fontweight='bold')\n",
    "plt.xticks(range(len(label_counts)), rotation=90)\n",
    "plt.yticks(np.arange(0, max(label_counts)+40,40))\n",
    "# Add a line indicating the average\n",
    "plt.axhline(min_samples_count, color='green', linestyle='--', label=f'Min: {min_samples_count:.2f}', linewidth = 2.0)\n",
    "plt.axhline(mean_samples_count, color='red', linestyle='--', label=f'Mean: {mean_samples_count:.2f}', linewidth = 2.0)\n",
    "plt.axhline(max_samples_count, color='purple', linestyle='--', label=f'Max: {max_samples_count:.2f}', linewidth = 2.0)\n",
    "plt.legend()\n",
    "# Show the plot\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 658,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import StandardScaler\n",
    "# Create a StandardScaler instance\n",
    "scaler = StandardScaler()\n",
    "# Fit the scaler to your data and transform the data\n",
    "scaled_data = scaler.fit_transform(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 659,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 5,
     "status": "ok",
     "timestamp": 1696350319768,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "PyAz8ry4UXL2",
    "outputId": "e895164f-a37c-4681-99e4-651d63110c2b"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " ...\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]\n",
      " [0 0 0 ... 0 0 0]]\n",
      "(167294, 233)\n"
     ]
    }
   ],
   "source": [
    "#Convert label to binary\n",
    "lb = LabelBinarizer()\n",
    "label = lb.fit_transform(label)\n",
    "print(label)\n",
    "print(label.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 660,
   "metadata": {
    "id": "a2lXX-PSUc-v"
   },
   "outputs": [],
   "source": [
    "# # #over/under sampling\n",
    "# from imblearn.over_sampling import SMOTE\n",
    "# smote = SMOTE()\n",
    "# scaled_data, label = smote.fit_resample(scaled_data, label)\n",
    "# print(scaled_data.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 661,
   "metadata": {
    "executionInfo": {
     "elapsed": 447,
     "status": "ok",
     "timestamp": 1696350433157,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "02g8cqJwUh9B"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X_test:  26767\n",
      "X_valid:  33459\n",
      "X_train:  107068\n"
     ]
    }
   ],
   "source": [
    "# X is the feature matrix and y is the label vector\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(scaled_data, label, test_size=0.2)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_train, y_train, test_size=0.2)\n",
    "\n",
    "#change the dimmension for CNN1D input\n",
    "X_train=X_train.reshape(X_train.shape[0], X_train.shape[1], 1)\n",
    "X_test=X_test.reshape(X_test.shape[0], X_test.shape[1], 1)\n",
    "X_valid=X_valid.reshape(X_valid.shape[0], X_valid.shape[1], 1)\n",
    "\n",
    "_, input_shape,_ = X_train.shape\n",
    "_, n_class = y_train.shape\n",
    "\n",
    "\n",
    "print('X_test: ', len(X_test))\n",
    "print('X_valid: ', len(X_valid))\n",
    "print('X_train: ', len(X_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(26767, 233)\n",
      "(33459, 233)\n",
      "(107068, 233)\n",
      "233\n"
     ]
    }
   ],
   "source": [
    "print(y_test.shape)\n",
    "print(y_valid.shape)\n",
    "print(y_train.shape)\n",
    "print(n_class)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 663,
   "metadata": {
    "executionInfo": {
     "elapsed": 1842,
     "status": "ok",
     "timestamp": 1696350438914,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "GjNRXW--U-Wq",
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_38\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " conv1d_76 (Conv1D)          (None, 150, 16)           64        \n",
      "                                                                 \n",
      " max_pooling1d_76 (MaxPooli  (None, 75, 16)            0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " conv1d_77 (Conv1D)          (None, 75, 32)            1568      \n",
      "                                                                 \n",
      " max_pooling1d_77 (MaxPooli  (None, 37, 32)            0         \n",
      " ng1D)                                                           \n",
      "                                                                 \n",
      " flatten_38 (Flatten)        (None, 1184)              0         \n",
      "                                                                 \n",
      " dense_76 (Dense)            (None, 100)               118500    \n",
      "                                                                 \n",
      " dropout_38 (Dropout)        (None, 100)               0         \n",
      "                                                                 \n",
      " dense_77 (Dense)            (None, 233)               23533     \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 143665 (561.19 KB)\n",
      "Trainable params: 143665 (561.19 KB)\n",
      "Non-trainable params: 0 (0.00 Byte)\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "#MODEL 1\n",
    "# define our Convolutional Neural Network architecture\n",
    "model = Sequential()\n",
    "# model.add(Conv1D(16, 3, padding=\"same\", activation=\"relu\", input_shape=(136, 1)))\n",
    "model.add(Conv1D(16, 3, activation=\"relu\", input_shape=(input_shape, 1), padding='same'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Conv1D(32, 3, activation='relu', padding='same'))\n",
    "model.add(MaxPooling1D(pool_size=2))\n",
    "# model.add(Conv1D(64, 3, activation='relu', padding='same'))\n",
    "# model.add(MaxPooling1D(pool_size=2))\n",
    "# model.add(Conv1D(128, 3, activation='relu', padding='same'))\n",
    "# model.add(MaxPooling1D(pool_size=2))\n",
    "# model.add(Conv1D(256, 3, activation='relu', padding='same'))\n",
    "# model.add(MaxPooling1D(pool_size=2))\n",
    "# model.add(Conv1D(512, 3, activation='relu', padding='same'))\n",
    "# model.add(MaxPooling1D(pool_size=2))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(100, activation='relu'))\n",
    "# model.add(Dropout(0.3))\n",
    "model.add(Dropout(0.2))\n",
    "# model.add(Dense(32, activation='relu'))\n",
    "# model.add(Dense(128, activation='relu'))\n",
    "model.add(Dense(n_class, activation='softmax'))\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "es = EarlyStopping(monitor='loss', patience=5, mode='auto', restore_best_weights=True)\n",
    "checkpoint = ModelCheckpoint('mix3-233-max-v3.h5',\n",
    "                             monitor='val_accuracy', verbose=0, save_best_only=True, mode='auto')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "executionInfo": {
     "elapsed": 14481317,
     "status": "ok",
     "timestamp": 1696364922170,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "DC_ySF6jVE91",
    "outputId": "cea98985-5d3d-4e24-d160-73e0d25212c5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "3346/3346 [==============================] - 11s 3ms/step - loss: 3.7861 - accuracy: 0.1826 - val_loss: 2.0887 - val_accuracy: 0.5295\n",
      "Epoch 2/500\n",
      "  55/3346 [..............................] - ETA: 9s - loss: 2.4534 - accuracy: 0.3665"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aimedic\\anaconda3\\envs\\ecg_Project\\lib\\site-packages\\keras\\src\\engine\\training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3346/3346 [==============================] - 10s 3ms/step - loss: 1.8743 - accuracy: 0.5133 - val_loss: 1.0960 - val_accuracy: 0.7712\n",
      "Epoch 3/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 1.3815 - accuracy: 0.6300 - val_loss: 0.8578 - val_accuracy: 0.8127\n",
      "Epoch 4/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 1.1709 - accuracy: 0.6799 - val_loss: 0.7538 - val_accuracy: 0.8222\n",
      "Epoch 5/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 1.0531 - accuracy: 0.7065 - val_loss: 0.6583 - val_accuracy: 0.8334\n",
      "Epoch 6/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.9621 - accuracy: 0.7278 - val_loss: 0.5641 - val_accuracy: 0.8583\n",
      "Epoch 7/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.9166 - accuracy: 0.7422 - val_loss: 0.5277 - val_accuracy: 0.8702\n",
      "Epoch 8/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.8550 - accuracy: 0.7548 - val_loss: 0.5113 - val_accuracy: 0.8722\n",
      "Epoch 9/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.8108 - accuracy: 0.7656 - val_loss: 0.5127 - val_accuracy: 0.8634\n",
      "Epoch 10/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.7811 - accuracy: 0.7730 - val_loss: 0.4654 - val_accuracy: 0.8740\n",
      "Epoch 11/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.7508 - accuracy: 0.7811 - val_loss: 0.4255 - val_accuracy: 0.8886\n",
      "Epoch 12/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.7265 - accuracy: 0.7895 - val_loss: 0.4201 - val_accuracy: 0.8969\n",
      "Epoch 13/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.6938 - accuracy: 0.7970 - val_loss: 0.4176 - val_accuracy: 0.8968\n",
      "Epoch 14/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.6751 - accuracy: 0.7980 - val_loss: 0.3535 - val_accuracy: 0.9171\n",
      "Epoch 15/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.6549 - accuracy: 0.8055 - val_loss: 0.3547 - val_accuracy: 0.8978\n",
      "Epoch 16/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.6326 - accuracy: 0.8119 - val_loss: 0.3534 - val_accuracy: 0.9108\n",
      "Epoch 17/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.6099 - accuracy: 0.8188 - val_loss: 0.3745 - val_accuracy: 0.8920\n",
      "Epoch 18/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.5831 - accuracy: 0.8249 - val_loss: 0.3299 - val_accuracy: 0.9102\n",
      "Epoch 19/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.5633 - accuracy: 0.8311 - val_loss: 0.3429 - val_accuracy: 0.8937\n",
      "Epoch 20/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.5490 - accuracy: 0.8356 - val_loss: 0.3150 - val_accuracy: 0.9103\n",
      "Epoch 21/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.5413 - accuracy: 0.8361 - val_loss: 0.2646 - val_accuracy: 0.9391\n",
      "Epoch 22/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.5230 - accuracy: 0.8404 - val_loss: 0.2658 - val_accuracy: 0.9294\n",
      "Epoch 23/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.5080 - accuracy: 0.8460 - val_loss: 0.2516 - val_accuracy: 0.9390\n",
      "Epoch 24/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4949 - accuracy: 0.8505 - val_loss: 0.2131 - val_accuracy: 0.9493\n",
      "Epoch 25/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4896 - accuracy: 0.8527 - val_loss: 0.2257 - val_accuracy: 0.9511\n",
      "Epoch 26/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4823 - accuracy: 0.8536 - val_loss: 0.2969 - val_accuracy: 0.9287\n",
      "Epoch 27/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4741 - accuracy: 0.8575 - val_loss: 0.2655 - val_accuracy: 0.9318\n",
      "Epoch 28/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4543 - accuracy: 0.8620 - val_loss: 0.2503 - val_accuracy: 0.9392\n",
      "Epoch 29/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4460 - accuracy: 0.8630 - val_loss: 0.2577 - val_accuracy: 0.9304\n",
      "Epoch 30/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4391 - accuracy: 0.8667 - val_loss: 0.2275 - val_accuracy: 0.9390\n",
      "Epoch 31/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4330 - accuracy: 0.8679 - val_loss: 0.2159 - val_accuracy: 0.9442\n",
      "Epoch 32/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4160 - accuracy: 0.8733 - val_loss: 0.1747 - val_accuracy: 0.9620\n",
      "Epoch 33/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4092 - accuracy: 0.8749 - val_loss: 0.1981 - val_accuracy: 0.9476\n",
      "Epoch 34/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.4069 - accuracy: 0.8776 - val_loss: 0.1986 - val_accuracy: 0.9506\n",
      "Epoch 35/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3885 - accuracy: 0.8814 - val_loss: 0.2222 - val_accuracy: 0.9349\n",
      "Epoch 36/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3845 - accuracy: 0.8836 - val_loss: 0.1654 - val_accuracy: 0.9614\n",
      "Epoch 37/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3733 - accuracy: 0.8862 - val_loss: 0.1695 - val_accuracy: 0.9612\n",
      "Epoch 38/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3557 - accuracy: 0.8904 - val_loss: 0.1540 - val_accuracy: 0.9672\n",
      "Epoch 39/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3595 - accuracy: 0.8932 - val_loss: 0.1603 - val_accuracy: 0.9637\n",
      "Epoch 40/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3481 - accuracy: 0.8957 - val_loss: 0.1626 - val_accuracy: 0.9611\n",
      "Epoch 41/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3431 - accuracy: 0.8940 - val_loss: 0.1881 - val_accuracy: 0.9558\n",
      "Epoch 42/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3222 - accuracy: 0.9011 - val_loss: 0.1833 - val_accuracy: 0.9568\n",
      "Epoch 43/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3275 - accuracy: 0.9000 - val_loss: 0.1519 - val_accuracy: 0.9645\n",
      "Epoch 44/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3089 - accuracy: 0.9053 - val_loss: 0.1568 - val_accuracy: 0.9636\n",
      "Epoch 45/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3171 - accuracy: 0.9061 - val_loss: 0.1456 - val_accuracy: 0.9683\n",
      "Epoch 46/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.3100 - accuracy: 0.9048 - val_loss: 0.1270 - val_accuracy: 0.9720\n",
      "Epoch 47/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2968 - accuracy: 0.9082 - val_loss: 0.1664 - val_accuracy: 0.9599\n",
      "Epoch 48/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2988 - accuracy: 0.9084 - val_loss: 0.1372 - val_accuracy: 0.9700\n",
      "Epoch 49/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2917 - accuracy: 0.9086 - val_loss: 0.1516 - val_accuracy: 0.9637\n",
      "Epoch 50/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2840 - accuracy: 0.9128 - val_loss: 0.1589 - val_accuracy: 0.9629\n",
      "Epoch 51/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2872 - accuracy: 0.9120 - val_loss: 0.1430 - val_accuracy: 0.9645\n",
      "Epoch 52/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2739 - accuracy: 0.9162 - val_loss: 0.1338 - val_accuracy: 0.9670\n",
      "Epoch 53/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2743 - accuracy: 0.9160 - val_loss: 0.1225 - val_accuracy: 0.9729\n",
      "Epoch 54/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2623 - accuracy: 0.9196 - val_loss: 0.1081 - val_accuracy: 0.9795\n",
      "Epoch 55/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2659 - accuracy: 0.9171 - val_loss: 0.1156 - val_accuracy: 0.9765\n",
      "Epoch 56/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2590 - accuracy: 0.9201 - val_loss: 0.1095 - val_accuracy: 0.9779\n",
      "Epoch 57/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2521 - accuracy: 0.9233 - val_loss: 0.1287 - val_accuracy: 0.9719\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 58/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2516 - accuracy: 0.9219 - val_loss: 0.1263 - val_accuracy: 0.9707\n",
      "Epoch 59/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2508 - accuracy: 0.9235 - val_loss: 0.1726 - val_accuracy: 0.9530\n",
      "Epoch 60/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2463 - accuracy: 0.9248 - val_loss: 0.0982 - val_accuracy: 0.9796\n",
      "Epoch 61/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2459 - accuracy: 0.9251 - val_loss: 0.1138 - val_accuracy: 0.9760\n",
      "Epoch 62/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2372 - accuracy: 0.9264 - val_loss: 0.1104 - val_accuracy: 0.9804\n",
      "Epoch 63/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2319 - accuracy: 0.9268 - val_loss: 0.1192 - val_accuracy: 0.9772\n",
      "Epoch 64/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2381 - accuracy: 0.9270 - val_loss: 0.1055 - val_accuracy: 0.9799\n",
      "Epoch 65/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2306 - accuracy: 0.9303 - val_loss: 0.1011 - val_accuracy: 0.9830\n",
      "Epoch 66/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2245 - accuracy: 0.9307 - val_loss: 0.1137 - val_accuracy: 0.9753\n",
      "Epoch 67/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2235 - accuracy: 0.9297 - val_loss: 0.0959 - val_accuracy: 0.9842\n",
      "Epoch 68/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2219 - accuracy: 0.9315 - val_loss: 0.1068 - val_accuracy: 0.9770\n",
      "Epoch 69/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2173 - accuracy: 0.9316 - val_loss: 0.1006 - val_accuracy: 0.9781\n",
      "Epoch 70/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2184 - accuracy: 0.9340 - val_loss: 0.1218 - val_accuracy: 0.9776\n",
      "Epoch 71/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2184 - accuracy: 0.9365 - val_loss: 0.1132 - val_accuracy: 0.9774\n",
      "Epoch 72/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2160 - accuracy: 0.9328 - val_loss: 0.0964 - val_accuracy: 0.9831\n",
      "Epoch 73/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2068 - accuracy: 0.9360 - val_loss: 0.1034 - val_accuracy: 0.9836\n",
      "Epoch 74/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2146 - accuracy: 0.9344 - val_loss: 0.1077 - val_accuracy: 0.9798\n",
      "Epoch 75/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2013 - accuracy: 0.9380 - val_loss: 0.1147 - val_accuracy: 0.9779\n",
      "Epoch 76/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2069 - accuracy: 0.9369 - val_loss: 0.0881 - val_accuracy: 0.9876\n",
      "Epoch 77/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.2023 - accuracy: 0.9374 - val_loss: 0.0949 - val_accuracy: 0.9863\n",
      "Epoch 78/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1986 - accuracy: 0.9377 - val_loss: 0.1017 - val_accuracy: 0.9814\n",
      "Epoch 79/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1954 - accuracy: 0.9384 - val_loss: 0.0904 - val_accuracy: 0.9856\n",
      "Epoch 80/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1900 - accuracy: 0.9403 - val_loss: 0.0911 - val_accuracy: 0.9830\n",
      "Epoch 81/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1990 - accuracy: 0.9387 - val_loss: 0.0956 - val_accuracy: 0.9811\n",
      "Epoch 82/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1934 - accuracy: 0.9396 - val_loss: 0.0911 - val_accuracy: 0.9817\n",
      "Epoch 83/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1931 - accuracy: 0.9407 - val_loss: 0.0829 - val_accuracy: 0.9852\n",
      "Epoch 84/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1885 - accuracy: 0.9404 - val_loss: 0.0941 - val_accuracy: 0.9823\n",
      "Epoch 85/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1898 - accuracy: 0.9411 - val_loss: 0.0922 - val_accuracy: 0.9803\n",
      "Epoch 86/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1863 - accuracy: 0.9413 - val_loss: 0.0776 - val_accuracy: 0.9866\n",
      "Epoch 87/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1915 - accuracy: 0.9409 - val_loss: 0.0752 - val_accuracy: 0.9860\n",
      "Epoch 88/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1831 - accuracy: 0.9424 - val_loss: 0.1064 - val_accuracy: 0.9762\n",
      "Epoch 89/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1832 - accuracy: 0.9437 - val_loss: 0.0812 - val_accuracy: 0.9817\n",
      "Epoch 90/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1821 - accuracy: 0.9440 - val_loss: 0.0655 - val_accuracy: 0.9871\n",
      "Epoch 91/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1781 - accuracy: 0.9442 - val_loss: 0.0973 - val_accuracy: 0.9770\n",
      "Epoch 92/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1797 - accuracy: 0.9444 - val_loss: 0.0844 - val_accuracy: 0.9811\n",
      "Epoch 93/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1763 - accuracy: 0.9459 - val_loss: 0.1319 - val_accuracy: 0.9664\n",
      "Epoch 94/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1744 - accuracy: 0.9454 - val_loss: 0.0801 - val_accuracy: 0.9859\n",
      "Epoch 95/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1774 - accuracy: 0.9447 - val_loss: 0.0897 - val_accuracy: 0.9821\n",
      "Epoch 96/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1729 - accuracy: 0.9468 - val_loss: 0.1261 - val_accuracy: 0.9684\n",
      "Epoch 97/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1719 - accuracy: 0.9468 - val_loss: 0.0750 - val_accuracy: 0.9857\n",
      "Epoch 98/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1713 - accuracy: 0.9459 - val_loss: 0.0836 - val_accuracy: 0.9831\n",
      "Epoch 99/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1699 - accuracy: 0.9468 - val_loss: 0.1226 - val_accuracy: 0.9718\n",
      "Epoch 100/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1703 - accuracy: 0.9477 - val_loss: 0.0700 - val_accuracy: 0.9878\n",
      "Epoch 101/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1606 - accuracy: 0.9496 - val_loss: 0.0843 - val_accuracy: 0.9844\n",
      "Epoch 102/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1731 - accuracy: 0.9481 - val_loss: 0.0830 - val_accuracy: 0.9860\n",
      "Epoch 103/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1628 - accuracy: 0.9493 - val_loss: 0.0837 - val_accuracy: 0.9832\n",
      "Epoch 104/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1558 - accuracy: 0.9507 - val_loss: 0.0719 - val_accuracy: 0.9872\n",
      "Epoch 105/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1680 - accuracy: 0.9485 - val_loss: 0.0871 - val_accuracy: 0.9823\n",
      "Epoch 106/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1579 - accuracy: 0.9506 - val_loss: 0.0664 - val_accuracy: 0.9892\n",
      "Epoch 107/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1575 - accuracy: 0.9494 - val_loss: 0.0844 - val_accuracy: 0.9817\n",
      "Epoch 108/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1555 - accuracy: 0.9531 - val_loss: 0.0715 - val_accuracy: 0.9885\n",
      "Epoch 109/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1571 - accuracy: 0.9512 - val_loss: 0.0700 - val_accuracy: 0.9845\n",
      "Epoch 110/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1540 - accuracy: 0.9507 - val_loss: 0.0826 - val_accuracy: 0.9840\n",
      "Epoch 111/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1539 - accuracy: 0.9515 - val_loss: 0.0698 - val_accuracy: 0.9882\n",
      "Epoch 112/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1509 - accuracy: 0.9527 - val_loss: 0.0623 - val_accuracy: 0.9872\n",
      "Epoch 113/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1566 - accuracy: 0.9526 - val_loss: 0.0571 - val_accuracy: 0.9896\n",
      "Epoch 114/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1472 - accuracy: 0.9536 - val_loss: 0.0554 - val_accuracy: 0.9917\n",
      "Epoch 115/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1541 - accuracy: 0.9536 - val_loss: 0.0726 - val_accuracy: 0.9888\n",
      "Epoch 116/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1465 - accuracy: 0.9545 - val_loss: 0.0588 - val_accuracy: 0.9904\n",
      "Epoch 117/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1459 - accuracy: 0.9532 - val_loss: 0.0574 - val_accuracy: 0.9868\n",
      "Epoch 118/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1525 - accuracy: 0.9529 - val_loss: 0.0605 - val_accuracy: 0.9888\n",
      "Epoch 119/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1512 - accuracy: 0.9541 - val_loss: 0.0861 - val_accuracy: 0.9801\n",
      "Epoch 120/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1465 - accuracy: 0.9543 - val_loss: 0.0549 - val_accuracy: 0.9912\n",
      "Epoch 121/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1471 - accuracy: 0.9549 - val_loss: 0.0654 - val_accuracy: 0.9861\n",
      "Epoch 122/500\n",
      "3346/3346 [==============================] - 10s 3ms/step - loss: 0.1472 - accuracy: 0.9551 - val_loss: 0.0477 - val_accuracy: 0.9896\n"
     ]
    }
   ],
   "source": [
    "#compile model\n",
    "opt = Adam(learning_rate=1e-3)\n",
    "# opt = Adam(lr=1e-3, decay=1e-3)\n",
    "# opt = SGD(learning_rate=0.01, momentum=0.3)\n",
    "# opt = RMSprop(learning_rate=0.001, momentum=0.9)\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=['accuracy'])\n",
    "#train model\n",
    "H = model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=500, \n",
    "              batch_size=32, callbacks=[es, checkpoint], validation_batch_size=32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 633,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 962
    },
    "executionInfo": {
     "elapsed": 4262,
     "status": "ok",
     "timestamp": 1695596599585,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "TUqP3WsQVKHm",
    "outputId": "ddf84024-b73d-43c3-a208-ed22f0e76db8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train-acc:  0.9382728934288025\n",
      "val-acc:  0.9848769903182983\n",
      "train-loss:  3.8635776042938232\n",
      "val-loss:  2.205944776535034\n"
     ]
    }
   ],
   "source": [
    "name_db=\"MIXED-III 233\"\n",
    "temp=\"max\"\n",
    "\n",
    "print(H.history.keys())\n",
    "# summarize history for accuracy\n",
    "fig, (ax1, ax2) = plt.subplots(1, 2)\n",
    "fig.set_size_inches([15,5])\n",
    "# fig.suptitle('NSRDB-18 subj without sample balancing-Do0.2-Bs32', fontsize=16)\n",
    "# fig.suptitle('NSRDB-18 subj #of beat=718(max)-Do0.2-Bs32', fontsize=16)\n",
    "fig.suptitle('{} subj #of beat=151({})'.format(name_db, temp), fontsize=16)\n",
    "\n",
    "ax1.plot(H.history['accuracy'])\n",
    "ax1.plot(H.history['val_accuracy'])\n",
    "# ax1.set_title('Accuracy')\n",
    "ax1.set_ylabel('Accuracy')\n",
    "ax1.set_xlabel('Number of Epoch')\n",
    "ax1.legend(['Training', 'Validation'], loc='lower right')\n",
    "ax1.grid()\n",
    "# ax1.set_legend(['Training', 'Validation'], loc='lower right')\n",
    "# plt.grid()\n",
    "# plt.savefig(\"Accuracy_70sample_model1_dwt_smote.png\")\n",
    "# plt.show()\n",
    "# summarize history for loss\n",
    "ax2.plot(H.history['loss'])\n",
    "ax2.plot(H.history['val_loss'])\n",
    "# ax2.set_title('loss')\n",
    "ax2.set_ylabel('Loss')\n",
    "ax2.set_xlabel('Number of Epoch')\n",
    "ax2.legend(['Training', 'Validation'], loc='upper right')\n",
    "plt.grid()\n",
    "plt.savefig(\"acc_loss_mix3_{}.png\".format(temp), bbox_inches=\"tight\", dpi=300)\n",
    "plt.show()\n",
    "\n",
    "print('train-acc: ', np.max(H.history['accuracy']))\n",
    "print('val-acc: ', np.max(H.history['val_accuracy']))\n",
    "print('train-loss: ', np.max(H.history['loss']))\n",
    "print('val-loss: ', np.max(H.history['val_loss']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 634,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 339
    },
    "executionInfo": {
     "elapsed": 44,
     "status": "error",
     "timestamp": 1695596599587,
     "user": {
      "displayName": "Ana rahma Yuniarti",
      "userId": "12691492895145241520"
     },
     "user_tz": -540
    },
    "id": "dw6pyPOQVRq2",
    "outputId": "01c6fd17-71a6-4853-c403-2e4bc11c7092"
   },
   "outputs": [],
   "source": [
    "#load model\n",
    "#model = tf.keras.models.load_model('best_weight_mix201-mean-do02-bs32.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 651,
   "metadata": {
    "id": "HjTiZw-TVYAy",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[INFO] evaluating network...\n",
      "583/583 [==============================] - 1s 1ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "         100     0.9867    1.0000    0.9933        74\n",
      "         101     0.5689    1.0000    0.7252        95\n",
      "         102     0.9892    1.0000    0.9946        92\n",
      "         103     0.9737    0.4684    0.6325        79\n",
      "         105     0.9894    1.0000    0.9947        93\n",
      "         106     0.4468    0.8750    0.5915        72\n",
      "         107     1.0000    1.0000    1.0000        79\n",
      "         108     0.9239    0.9770    0.9497        87\n",
      "         109     1.0000    1.0000    1.0000        82\n",
      "         111     0.9881    0.9326    0.9595        89\n",
      "         112     1.0000    0.9868    0.9934        76\n",
      "         113     1.0000    0.9459    0.9722        74\n",
      "         115     0.8554    0.9467    0.8987        75\n",
      "         116     1.0000    1.0000    1.0000        77\n",
      "         118     1.0000    0.9740    0.9868        77\n",
      "         119     1.0000    1.0000    1.0000        78\n",
      "         121     0.6698    0.9726    0.7933        73\n",
      "         122     1.0000    1.0000    1.0000        78\n",
      "         123     1.0000    1.0000    1.0000        76\n",
      "         124     0.9867    0.8605    0.9193        86\n",
      "       16265     1.0000    1.0000    1.0000        78\n",
      "       16272     1.0000    1.0000    1.0000        71\n",
      "       16273     1.0000    1.0000    1.0000        84\n",
      "       16420     1.0000    1.0000    1.0000        87\n",
      "       16483     1.0000    1.0000    1.0000        73\n",
      "       16539     1.0000    1.0000    1.0000        65\n",
      "       16773     1.0000    1.0000    1.0000        81\n",
      "       16786     1.0000    1.0000    1.0000        72\n",
      "       16795     1.0000    1.0000    1.0000        86\n",
      "       17052     1.0000    1.0000    1.0000        79\n",
      "       17453     1.0000    1.0000    1.0000        73\n",
      "       18177     1.0000    1.0000    1.0000        76\n",
      "       18184     0.9865    0.9865    0.9865        74\n",
      "       19088     1.0000    0.9896    0.9948        96\n",
      "       19090     1.0000    1.0000    1.0000        66\n",
      "       19093     1.0000    1.0000    1.0000        87\n",
      "       19140     1.0000    1.0000    1.0000        90\n",
      "       19830     1.0000    1.0000    1.0000        74\n",
      "         202     0.9565    0.5570    0.7040        79\n",
      "         205     1.0000    0.9452    0.9718        73\n",
      "         209     1.0000    0.9765    0.9881        85\n",
      "         212     0.9649    0.8333    0.8943        66\n",
      "         215     0.9634    1.0000    0.9814        79\n",
      "         220     0.0000    0.0000    0.0000        87\n",
      "         234     0.9022    0.9765    0.9379        85\n",
      "     3000397     0.7945    0.9062    0.8467        64\n",
      "     3003254     1.0000    1.0000    1.0000        80\n",
      "     3005819     0.9881    1.0000    0.9940        83\n",
      "     3006814     1.0000    1.0000    1.0000        65\n",
      "     3007104     0.9886    1.0000    0.9943        87\n",
      "     3012495     0.9889    0.9674    0.9780        92\n",
      "     3012618     1.0000    1.0000    1.0000        86\n",
      "     3065677     0.9853    1.0000    0.9926        67\n",
      "     3067306     0.9737    0.9610    0.9673        77\n",
      "     3067697     1.0000    1.0000    1.0000        76\n",
      "     3068722     1.0000    1.0000    1.0000        76\n",
      "     3071336     1.0000    1.0000    1.0000        71\n",
      "     3071381     1.0000    1.0000    1.0000        84\n",
      "     3071472     1.0000    1.0000    1.0000        55\n",
      "     3072690     0.9875    1.0000    0.9937        79\n",
      "     3073320     1.0000    1.0000    1.0000        79\n",
      "     3075431     0.9808    0.8947    0.9358        57\n",
      "     3075517     1.0000    1.0000    1.0000        83\n",
      "     3076908     0.8916    1.0000    0.9427        74\n",
      "     3077561     1.0000    1.0000    1.0000        75\n",
      "     3078482     1.0000    1.0000    1.0000        77\n",
      "     3078619     1.0000    0.9744    0.9870        78\n",
      "     3078732     1.0000    1.0000    1.0000        80\n",
      "     3096825     1.0000    0.9868    0.9934        76\n",
      "     3096845     1.0000    1.0000    1.0000        73\n",
      "     3097156     1.0000    0.9753    0.9875        81\n",
      "     3098205     0.7901    0.8649    0.8258        74\n",
      "     3099154     0.9778    0.9167    0.9462        96\n",
      "     3099745     1.0000    1.0000    1.0000        65\n",
      "     3105015     1.0000    1.0000    1.0000        84\n",
      "     3106149     0.9882    1.0000    0.9941        84\n",
      "     3167090     1.0000    0.9512    0.9750        82\n",
      "     3167494     1.0000    0.9750    0.9873        80\n",
      "     3167625     1.0000    1.0000    1.0000        83\n",
      "     3167702     0.9310    0.9101    0.9205        89\n",
      "     3169004     0.8427    0.9259    0.8824        81\n",
      "     3169960     0.9494    0.9615    0.9554        78\n",
      "     3170127     0.9390    0.9625    0.9506        80\n",
      "     3170913     1.0000    1.0000    1.0000        87\n",
      "     3171478     0.9722    0.9091    0.9396        77\n",
      "     3175800     1.0000    1.0000    1.0000        89\n",
      "     3176817     1.0000    0.9615    0.9804        78\n",
      "     3178071     1.0000    1.0000    1.0000        95\n",
      "     3178377     1.0000    1.0000    1.0000        74\n",
      "     3178573     1.0000    1.0000    1.0000        94\n",
      "     3178762     1.0000    1.0000    1.0000        82\n",
      "     3179652     0.9740    0.9615    0.9677        78\n",
      "     3179782     0.9518    1.0000    0.9753        79\n",
      "     3180443     1.0000    1.0000    1.0000        74\n",
      "     3180470     1.0000    1.0000    1.0000        85\n",
      "     3200516     0.9886    1.0000    0.9943        87\n",
      "     3310873     0.9762    1.0000    0.9880        82\n",
      "     3520180     1.0000    1.0000    1.0000        70\n",
      "     3522300     1.0000    1.0000    1.0000        72\n",
      "     3524794     1.0000    0.9878    0.9939        82\n",
      "     3589708     1.0000    1.0000    1.0000        78\n",
      "     3602498     0.9861    1.0000    0.9930        71\n",
      "     3606898     1.0000    1.0000    1.0000        84\n",
      "     3782531     1.0000    0.9875    0.9937        80\n",
      "     3782780     1.0000    1.0000    1.0000        82\n",
      "     3782854     1.0000    1.0000    1.0000        76\n",
      "     3783840     0.9663    1.0000    0.9829        86\n",
      "     3784063     0.9881    1.0000    0.9940        83\n",
      "     3784577     1.0000    1.0000    1.0000        80\n",
      "     3901254     1.0000    1.0000    1.0000        75\n",
      "     3901339     1.0000    1.0000    1.0000        70\n",
      "     3904726     0.9818    0.7105    0.8244        76\n",
      "     3905079     0.9865    0.9865    0.9865        74\n",
      "     3906712     1.0000    1.0000    1.0000        75\n",
      "     3913598     1.0000    1.0000    1.0000        79\n",
      "     3913851     1.0000    0.9891    0.9945        92\n",
      "     3917517     1.0000    1.0000    1.0000        75\n",
      "     3920718     1.0000    0.9880    0.9939        83\n",
      "     3921070     0.9643    1.0000    0.9818        81\n",
      "     3921634     1.0000    1.0000    1.0000        78\n",
      "     3928867     0.9844    0.9545    0.9692        66\n",
      "     3930538     1.0000    1.0000    1.0000        68\n",
      "     3931987     0.9773    1.0000    0.9885        86\n",
      "     3935809     1.0000    1.0000    1.0000        63\n",
      "     3939771     1.0000    1.0000    1.0000        87\n",
      "     3955576     1.0000    1.0000    1.0000        89\n",
      "     3964786     1.0000    0.9875    0.9937        80\n",
      "     3964898     0.9383    0.9744    0.9560        78\n",
      "     3966842     1.0000    1.0000    1.0000        84\n",
      "     3968979     0.9886    1.0000    0.9943        87\n",
      "     3970038     0.9438    0.9032    0.9231        93\n",
      "     3970760     0.9759    1.0000    0.9878        81\n",
      "     3970862     0.8354    0.9706    0.8980        68\n",
      "     3972877     0.9600    0.8182    0.8834        88\n",
      "     3974412     1.0000    0.9420    0.9701        69\n",
      "     3979929     1.0000    1.0000    1.0000        84\n",
      "     3981727     0.9114    0.9351    0.9231        77\n",
      "     3985507     0.9506    1.0000    0.9747        77\n",
      "     3986523     1.0000    0.9875    0.9937        80\n",
      "     3988011     1.0000    1.0000    1.0000        75\n",
      "     3988826     1.0000    0.9324    0.9650        74\n",
      "     3993363     0.9759    0.9759    0.9759        83\n",
      "     3999053     1.0000    0.9886    0.9943        88\n",
      "         P76     1.0000    1.0000    1.0000        75\n",
      "         p01     0.9853    0.9853    0.9853        68\n",
      "         p02     1.0000    0.9868    0.9934        76\n",
      "         p03     1.0000    0.9859    0.9929        71\n",
      "         p04     1.0000    0.9865    0.9932        74\n",
      "         p05     0.8961    1.0000    0.9452        69\n",
      "         p06     1.0000    0.9630    0.9811        81\n",
      "         p07     1.0000    0.9250    0.9610        80\n",
      "         p08     1.0000    1.0000    1.0000        78\n",
      "         p09     0.9740    1.0000    0.9868        75\n",
      "         p10     1.0000    1.0000    1.0000        72\n",
      "         p11     1.0000    1.0000    1.0000        71\n",
      "         p12     1.0000    0.9861    0.9930        72\n",
      "         p13     0.9560    1.0000    0.9775        87\n",
      "         p14     0.9655    1.0000    0.9825        84\n",
      "         p15     1.0000    0.9889    0.9944        90\n",
      "         p16     0.9583    0.9452    0.9517        73\n",
      "         p17     1.0000    1.0000    1.0000        81\n",
      "         p18     0.9600    0.9730    0.9664        74\n",
      "         p19     0.9798    0.9510    0.9652       102\n",
      "         p20     0.9333    0.7179    0.8116        78\n",
      "         p21     0.7767    0.8696    0.8205        92\n",
      "         p22     1.0000    1.0000    1.0000        98\n",
      "         p23     1.0000    1.0000    1.0000        75\n",
      "         p24     0.8764    0.9398    0.9070        83\n",
      "         p25     0.9726    0.9861    0.9793        72\n",
      "         p26     0.9868    1.0000    0.9934        75\n",
      "         p27     1.0000    0.9556    0.9773        90\n",
      "         p28     0.9897    1.0000    0.9948        96\n",
      "         p29     1.0000    0.9452    0.9718        73\n",
      "         p30     1.0000    1.0000    1.0000        71\n",
      "         p31     1.0000    0.9390    0.9686        82\n",
      "         p32     1.0000    0.9146    0.9554        82\n",
      "         p33     1.0000    0.9740    0.9868        77\n",
      "         p34     0.9667    0.9560    0.9613        91\n",
      "         p35     0.9762    1.0000    0.9880        82\n",
      "         p36     0.9780    1.0000    0.9889        89\n",
      "         p37     1.0000    1.0000    1.0000        71\n",
      "         p38     0.7857    0.9625    0.8652        80\n",
      "         p39     0.9861    0.9103    0.9467        78\n",
      "         p40     0.8519    0.9079    0.8790        76\n",
      "         p41     1.0000    1.0000    1.0000        85\n",
      "         p42     1.0000    0.9294    0.9634        85\n",
      "         p43     0.9615    0.8427    0.8982        89\n",
      "         p44     0.9744    0.8736    0.9212        87\n",
      "         p45     1.0000    0.9872    0.9935        78\n",
      "         p46     0.8072    0.9306    0.8645        72\n",
      "         p47     1.0000    0.9500    0.9744        80\n",
      "         p48     1.0000    1.0000    1.0000        85\n",
      "         p49     0.9579    1.0000    0.9785        91\n",
      "         p50     1.0000    0.8901    0.9419        91\n",
      "         p51     0.9747    0.9872    0.9809        78\n",
      "         p52     0.8710    0.9759    0.9205        83\n",
      "         p53     0.9535    0.9647    0.9591        85\n",
      "         p54     1.0000    1.0000    1.0000        62\n",
      "         p55     0.9750    0.9398    0.9571        83\n",
      "         p56     0.9659    1.0000    0.9827        85\n",
      "         p57     0.8929    0.8824    0.8876        85\n",
      "         p58     1.0000    0.9620    0.9806        79\n",
      "         p59     1.0000    0.9863    0.9931        73\n",
      "         p60     0.8333    0.8667    0.8497        75\n",
      "         p61     0.9398    0.9873    0.9630        79\n",
      "         p62     0.9512    0.8864    0.9176        88\n",
      "         p63     0.9737    0.8605    0.9136        86\n",
      "         p64     0.9889    1.0000    0.9944        89\n",
      "         p65     0.9239    0.9551    0.9392        89\n",
      "         p66     1.0000    1.0000    1.0000        80\n",
      "         p67     1.0000    1.0000    1.0000        84\n",
      "         p68     0.9775    0.9775    0.9775        89\n",
      "         p69     0.9277    0.9747    0.9506        79\n",
      "         p70     0.9649    0.7534    0.8462        73\n",
      "         p71     0.9846    0.8767    0.9275        73\n",
      "         p72     0.9506    0.9506    0.9506        81\n",
      "         p73     0.8300    1.0000    0.9071        83\n",
      "         p74     1.0000    0.9341    0.9659        91\n",
      "         p75     1.0000    1.0000    1.0000        90\n",
      "         p77     1.0000    0.9868    0.9934        76\n",
      "         p78     0.9892    1.0000    0.9946        92\n",
      "         p79     0.9677    1.0000    0.9836        90\n",
      "         p80     0.9892    0.9684    0.9787        95\n",
      "         p81     0.9059    1.0000    0.9506        77\n",
      "         p82     0.8478    1.0000    0.9176        78\n",
      "         p83     0.9556    1.0000    0.9773        86\n",
      "         p84     1.0000    1.0000    1.0000        89\n",
      "         p85     0.9802    0.9802    0.9802       101\n",
      "         p86     1.0000    0.9733    0.9865        75\n",
      "         p87     0.7917    0.9500    0.8636        80\n",
      "         p88     1.0000    1.0000    1.0000        75\n",
      "         p89     1.0000    1.0000    1.0000        84\n",
      "         p90     0.9620    1.0000    0.9806        76\n",
      "\n",
      "    accuracy                         0.9646     18640\n",
      "   macro avg     0.9659    0.9649    0.9634     18640\n",
      "weighted avg     0.9656    0.9646    0.9631     18640\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aimedic\\anaconda3\\envs\\ecg_Project\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\aimedic\\anaconda3\\envs\\ecg_Project\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "C:\\Users\\aimedic\\anaconda3\\envs\\ecg_Project\\lib\\site-packages\\sklearn\\metrics\\_classification.py:1469: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "# evaluate the network\n",
    "print(\"[INFO] evaluating network...\")\n",
    "predictions = model.predict(X_test, batch_size=32)\n",
    "target_names=list(map(str,lb.classes_))\n",
    "report = classification_report(y_test.argmax(axis=1), predictions.argmax(axis=1), target_names=target_names, digits=4)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 628,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from sklearn.metrics import roc_curve, roc_auc_score, auc\n",
    "\n",
    "#name_db=\"NSRDB 18\"\n",
    "#temp=\"mean\"\n",
    "\n",
    "y_pred_prob = np.squeeze(predictions)\n",
    "\n",
    "# Compute the micro-averaged ROC curve and AUC score\n",
    "fpr_micro, tpr_micro, _ = roc_curve(y_test.ravel(), y_pred_prob.ravel())\n",
    "roc_auc_micro = auc(fpr_micro, tpr_micro)\n",
    "np.save('np_fpr_{}_{}.npy'.format(name_db, temp), fpr_micro)\n",
    "np.save('np_tpr_{}_{}.npy'.format(name_db, temp), tpr_micro)\n",
    "\n",
    "# Plot the micro-averaged ROC curve\n",
    "plt.figure(figsize=(8, 6))\n",
    "plt.plot(fpr_micro, tpr_micro, color='darkorange', lw=2, label=f'ROC AUC Score = {roc_auc_micro:.2f}')\n",
    "plt.plot([0, 1], [0, 1], linestyle='--', color='gray', label='Random Guess')\n",
    "plt.xlabel('False Positive Rate')\n",
    "plt.ylabel('True Positive Rate')\n",
    "plt.title('ROC Curve of {} - {}'.format(name_db, temp))\n",
    "plt.legend(fontsize=\"14\")\n",
    "plt.savefig(\"Fig_ROC_{}_{}.png\".format(name_db, temp), dpi=300, bbox_inches='tight')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create or open a CSV file for writing\n",
    "import csv\n",
    "with open('report-nsrdb-18-max-do02-bs32.csv', 'w', newline='') as csvfile:\n",
    "    writer = csv.writer(csvfile)\n",
    "\n",
    "    # Split the classification report by lines\n",
    "    lines = report.split('\\n')\n",
    "    for line in lines:\n",
    "        # Split each line by whitespace\n",
    "        row = line.split()\n",
    "        writer.writerow(row)\n",
    "\n",
    "print(\"Classification report saved to 'classification_report.csv'\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "-lU2VCI2VcC3"
   },
   "outputs": [],
   "source": [
    "confusion_matrix = np.zeros((n_class, n_class))\n",
    "for i in range(len(predictions)):\n",
    "    predicted_class = predictions.argmax(axis=1)[i]\n",
    "    true_class = y_test.argmax(axis=1)[i]\n",
    "    confusion_matrix[true_class, predicted_class] += 1\n",
    "plt.imshow(confusion_matrix, cmap='Blues')\n",
    "plt.colorbar()\n",
    "plt.xlabel('Predicted Class')\n",
    "plt.ylabel('True Class')\n",
    "plt.title('Confusion Matrix')\n",
    "plt.show()\n",
    "# plt.savefig(\"Confussion_Matrix_model1_90person.png\", bbox_inches='tight')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# K-Folds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.callbacks import EarlyStopping, ModelCheckpoint\n",
    "es_k = EarlyStopping(monitor='loss', patience=5, mode='auto', restore_best_weights=True)\n",
    "checkpoint_k = ModelCheckpoint('best_weight_ecgid-wob-do02-bs32-kf5.h5',\n",
    "                             monitor='val_accuracy', verbose=0, save_best_only=True, mode='auto',)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "————————————————————————————————————\n",
      "Training for fold 1\n",
      "Epoch 1/500\n",
      "114/114 [==============================] - 1s 4ms/step - loss: 4.0669 - accuracy: 0.1122 - val_loss: 3.4055 - val_accuracy: 0.2252\n",
      "Epoch 2/500\n",
      " 63/114 [===============>..............] - ETA: 0s - loss: 3.0460 - accuracy: 0.2857"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\aimedic\\anaconda3\\envs\\ecg_Project\\lib\\site-packages\\keras\\src\\engine\\training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114/114 [==============================] - 0s 3ms/step - loss: 2.7235 - accuracy: 0.3466 - val_loss: 1.8382 - val_accuracy: 0.5306\n",
      "Epoch 3/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 1.7092 - accuracy: 0.5356 - val_loss: 1.1583 - val_accuracy: 0.7129\n",
      "Epoch 4/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 1.3902 - accuracy: 0.6407 - val_loss: 0.9059 - val_accuracy: 0.7650\n",
      "Epoch 5/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.9844 - accuracy: 0.7139 - val_loss: 0.6657 - val_accuracy: 0.8290\n",
      "Epoch 6/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.8044 - accuracy: 0.7664 - val_loss: 0.5384 - val_accuracy: 0.8705\n",
      "Epoch 7/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.6718 - accuracy: 0.8014 - val_loss: 0.4561 - val_accuracy: 0.8867\n",
      "Epoch 8/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5599 - accuracy: 0.8341 - val_loss: 0.3910 - val_accuracy: 0.9036\n",
      "Epoch 9/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5014 - accuracy: 0.8454 - val_loss: 0.3457 - val_accuracy: 0.9289\n",
      "Epoch 10/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4972 - accuracy: 0.8691 - val_loss: 0.3199 - val_accuracy: 0.9275\n",
      "Epoch 11/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4081 - accuracy: 0.8713 - val_loss: 0.3132 - val_accuracy: 0.9310\n",
      "Epoch 12/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3435 - accuracy: 0.8908 - val_loss: 0.2779 - val_accuracy: 0.9437\n",
      "Epoch 13/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4191 - accuracy: 0.8955 - val_loss: 0.2754 - val_accuracy: 0.9472\n",
      "Epoch 14/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3041 - accuracy: 0.9062 - val_loss: 0.2655 - val_accuracy: 0.9444\n",
      "Epoch 15/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2553 - accuracy: 0.9161 - val_loss: 0.2525 - val_accuracy: 0.9521\n",
      "Epoch 16/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2367 - accuracy: 0.9285 - val_loss: 0.2599 - val_accuracy: 0.9451\n",
      "Epoch 17/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2194 - accuracy: 0.9354 - val_loss: 0.2318 - val_accuracy: 0.9550\n",
      "Epoch 18/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2030 - accuracy: 0.9384 - val_loss: 0.2276 - val_accuracy: 0.9585\n",
      "Epoch 19/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2052 - accuracy: 0.9329 - val_loss: 0.2446 - val_accuracy: 0.9493\n",
      "Epoch 20/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1919 - accuracy: 0.9409 - val_loss: 0.2165 - val_accuracy: 0.9599\n",
      "Epoch 21/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1609 - accuracy: 0.9461 - val_loss: 0.2419 - val_accuracy: 0.9536\n",
      "Epoch 22/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2422 - accuracy: 0.9348 - val_loss: 0.2783 - val_accuracy: 0.9451\n",
      "Epoch 23/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1874 - accuracy: 0.9444 - val_loss: 0.2229 - val_accuracy: 0.9599\n",
      "Epoch 24/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1542 - accuracy: 0.9499 - val_loss: 0.2251 - val_accuracy: 0.9620\n",
      "Epoch 25/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1392 - accuracy: 0.9532 - val_loss: 0.2196 - val_accuracy: 0.9648\n",
      "Epoch 26/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1290 - accuracy: 0.9593 - val_loss: 0.2101 - val_accuracy: 0.9669\n",
      "Epoch 27/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1452 - accuracy: 0.9535 - val_loss: 0.2087 - val_accuracy: 0.9683\n",
      "Epoch 28/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1172 - accuracy: 0.9659 - val_loss: 0.1940 - val_accuracy: 0.9704\n",
      "Epoch 29/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1256 - accuracy: 0.9604 - val_loss: 0.2170 - val_accuracy: 0.9648\n",
      "Epoch 30/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1027 - accuracy: 0.9706 - val_loss: 0.1988 - val_accuracy: 0.9662\n",
      "Epoch 31/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0987 - accuracy: 0.9667 - val_loss: 0.2037 - val_accuracy: 0.9669\n",
      "Epoch 32/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1004 - accuracy: 0.9664 - val_loss: 0.2148 - val_accuracy: 0.9634\n",
      "Epoch 33/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0961 - accuracy: 0.9711 - val_loss: 0.2155 - val_accuracy: 0.9613\n",
      "Epoch 34/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0869 - accuracy: 0.9717 - val_loss: 0.2106 - val_accuracy: 0.9697\n",
      "Epoch 35/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0909 - accuracy: 0.9700 - val_loss: 0.2126 - val_accuracy: 0.9704\n",
      "Epoch 36/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0871 - accuracy: 0.9681 - val_loss: 0.2236 - val_accuracy: 0.9613\n",
      "Epoch 37/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0832 - accuracy: 0.9714 - val_loss: 0.2280 - val_accuracy: 0.9634\n",
      "Epoch 38/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0865 - accuracy: 0.9708 - val_loss: 0.1971 - val_accuracy: 0.9726\n",
      "Epoch 39/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0977 - accuracy: 0.9664 - val_loss: 0.2221 - val_accuracy: 0.9690\n",
      "Epoch 40/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0826 - accuracy: 0.9706 - val_loss: 0.2155 - val_accuracy: 0.9740\n",
      "Epoch 41/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0741 - accuracy: 0.9763 - val_loss: 0.2495 - val_accuracy: 0.9655\n",
      "Epoch 42/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0752 - accuracy: 0.9769 - val_loss: 0.2118 - val_accuracy: 0.9704\n",
      "Epoch 43/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0783 - accuracy: 0.9741 - val_loss: 0.2207 - val_accuracy: 0.9676\n",
      "Epoch 44/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0710 - accuracy: 0.9769 - val_loss: 0.2304 - val_accuracy: 0.9676\n",
      "Epoch 45/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0738 - accuracy: 0.9750 - val_loss: 0.2316 - val_accuracy: 0.9726\n",
      "Epoch 46/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0723 - accuracy: 0.9772 - val_loss: 0.2225 - val_accuracy: 0.9683\n",
      "Epoch 47/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0587 - accuracy: 0.9799 - val_loss: 0.2301 - val_accuracy: 0.9704\n",
      "Epoch 48/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1224 - accuracy: 0.9692 - val_loss: 0.3125 - val_accuracy: 0.9543\n",
      "Epoch 49/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0880 - accuracy: 0.9700 - val_loss: 0.2343 - val_accuracy: 0.9690\n",
      "Epoch 50/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0758 - accuracy: 0.9739 - val_loss: 0.2129 - val_accuracy: 0.9733\n",
      "Epoch 51/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0746 - accuracy: 0.9717 - val_loss: 0.2265 - val_accuracy: 0.9747\n",
      "Epoch 52/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0651 - accuracy: 0.9805 - val_loss: 0.2069 - val_accuracy: 0.9754\n",
      "Score for fold 1: loss of 0.2329455465078354; accuracy of 95.59956192970276%\n",
      "————————————————————————————————————\n",
      "Training for fold 2\n",
      "Epoch 1/500\n",
      "114/114 [==============================] - 1s 4ms/step - loss: 3.1073 - accuracy: 0.2850 - val_loss: 1.5787 - val_accuracy: 0.6045\n",
      "Epoch 2/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 1.3927 - accuracy: 0.6187 - val_loss: 0.8502 - val_accuracy: 0.7530\n",
      "Epoch 3/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.9261 - accuracy: 0.7238 - val_loss: 0.5714 - val_accuracy: 0.8431\n",
      "Epoch 4/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.7321 - accuracy: 0.7890 - val_loss: 0.4531 - val_accuracy: 0.9015\n",
      "Epoch 5/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5638 - accuracy: 0.8336 - val_loss: 0.3700 - val_accuracy: 0.9071\n",
      "Epoch 6/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5545 - accuracy: 0.8553 - val_loss: 0.3348 - val_accuracy: 0.9346\n",
      "Epoch 7/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4209 - accuracy: 0.8740 - val_loss: 0.3018 - val_accuracy: 0.9331\n",
      "Epoch 8/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3837 - accuracy: 0.8825 - val_loss: 0.2610 - val_accuracy: 0.9486\n",
      "Epoch 9/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3181 - accuracy: 0.9059 - val_loss: 0.2373 - val_accuracy: 0.9529\n",
      "Epoch 10/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3089 - accuracy: 0.9040 - val_loss: 0.2292 - val_accuracy: 0.9599\n",
      "Epoch 11/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3205 - accuracy: 0.9230 - val_loss: 0.2436 - val_accuracy: 0.9529\n",
      "Epoch 12/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2592 - accuracy: 0.9257 - val_loss: 0.2140 - val_accuracy: 0.9620\n",
      "Epoch 13/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2193 - accuracy: 0.9318 - val_loss: 0.2090 - val_accuracy: 0.9641\n",
      "Epoch 14/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2155 - accuracy: 0.9351 - val_loss: 0.2026 - val_accuracy: 0.9655\n",
      "Epoch 15/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1876 - accuracy: 0.9464 - val_loss: 0.2001 - val_accuracy: 0.9620\n",
      "Epoch 16/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1906 - accuracy: 0.9365 - val_loss: 0.1999 - val_accuracy: 0.9627\n",
      "Epoch 17/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1690 - accuracy: 0.9436 - val_loss: 0.1871 - val_accuracy: 0.9669\n",
      "Epoch 18/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1537 - accuracy: 0.9538 - val_loss: 0.1753 - val_accuracy: 0.9697\n",
      "Epoch 19/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1498 - accuracy: 0.9541 - val_loss: 0.1906 - val_accuracy: 0.9683\n",
      "Epoch 20/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1284 - accuracy: 0.9596 - val_loss: 0.1776 - val_accuracy: 0.9697\n",
      "Epoch 21/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1363 - accuracy: 0.9549 - val_loss: 0.1740 - val_accuracy: 0.9683\n",
      "Epoch 22/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1225 - accuracy: 0.9601 - val_loss: 0.1952 - val_accuracy: 0.9634\n",
      "Epoch 23/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1215 - accuracy: 0.9565 - val_loss: 0.1769 - val_accuracy: 0.9711\n",
      "Epoch 24/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1449 - accuracy: 0.9538 - val_loss: 0.1817 - val_accuracy: 0.9676\n",
      "Epoch 25/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1205 - accuracy: 0.9598 - val_loss: 0.1763 - val_accuracy: 0.9683\n",
      "Epoch 26/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1032 - accuracy: 0.9684 - val_loss: 0.1885 - val_accuracy: 0.9690\n",
      "Epoch 27/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0907 - accuracy: 0.9675 - val_loss: 0.2097 - val_accuracy: 0.9634\n",
      "Epoch 28/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1077 - accuracy: 0.9678 - val_loss: 0.1843 - val_accuracy: 0.9711\n",
      "Epoch 29/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0839 - accuracy: 0.9741 - val_loss: 0.1952 - val_accuracy: 0.9697\n",
      "Epoch 30/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0965 - accuracy: 0.9686 - val_loss: 0.1957 - val_accuracy: 0.9726\n",
      "Epoch 31/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1046 - accuracy: 0.9670 - val_loss: 0.1855 - val_accuracy: 0.9690\n",
      "Epoch 32/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0885 - accuracy: 0.9717 - val_loss: 0.1872 - val_accuracy: 0.9655\n",
      "Epoch 33/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0802 - accuracy: 0.9728 - val_loss: 0.1956 - val_accuracy: 0.9683\n",
      "Epoch 34/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0728 - accuracy: 0.9777 - val_loss: 0.2015 - val_accuracy: 0.9676\n",
      "Epoch 35/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0986 - accuracy: 0.9711 - val_loss: 0.2117 - val_accuracy: 0.9627\n",
      "Epoch 36/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0957 - accuracy: 0.9692 - val_loss: 0.1974 - val_accuracy: 0.9704\n",
      "Epoch 37/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0742 - accuracy: 0.9733 - val_loss: 0.1894 - val_accuracy: 0.9719\n",
      "Epoch 38/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0741 - accuracy: 0.9747 - val_loss: 0.1866 - val_accuracy: 0.9726\n",
      "Epoch 39/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0793 - accuracy: 0.9728 - val_loss: 0.1758 - val_accuracy: 0.9747\n",
      "Score for fold 2: loss of 0.2925639748573303; accuracy of 96.14961743354797%\n",
      "————————————————————————————————————\n",
      "Training for fold 3\n",
      "Epoch 1/500\n",
      "114/114 [==============================] - 1s 4ms/step - loss: 3.0157 - accuracy: 0.3048 - val_loss: 1.4872 - val_accuracy: 0.5827\n",
      "Epoch 2/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 1.3158 - accuracy: 0.6388 - val_loss: 0.8112 - val_accuracy: 0.7973\n",
      "Epoch 3/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.8811 - accuracy: 0.7516 - val_loss: 0.5890 - val_accuracy: 0.8494\n",
      "Epoch 4/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.6691 - accuracy: 0.8025 - val_loss: 0.4836 - val_accuracy: 0.8874\n",
      "Epoch 5/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5491 - accuracy: 0.8454 - val_loss: 0.4223 - val_accuracy: 0.9001\n",
      "Epoch 6/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4783 - accuracy: 0.8633 - val_loss: 0.3831 - val_accuracy: 0.9134\n",
      "Epoch 7/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3976 - accuracy: 0.8762 - val_loss: 0.3449 - val_accuracy: 0.9324\n",
      "Epoch 8/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3716 - accuracy: 0.8817 - val_loss: 0.3161 - val_accuracy: 0.9416\n",
      "Epoch 9/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3193 - accuracy: 0.9023 - val_loss: 0.3145 - val_accuracy: 0.9388\n",
      "Epoch 10/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3051 - accuracy: 0.9070 - val_loss: 0.2952 - val_accuracy: 0.9437\n",
      "Epoch 11/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2583 - accuracy: 0.9183 - val_loss: 0.2740 - val_accuracy: 0.9507\n",
      "Epoch 12/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2404 - accuracy: 0.9230 - val_loss: 0.2805 - val_accuracy: 0.9465\n",
      "Epoch 13/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2222 - accuracy: 0.9265 - val_loss: 0.2706 - val_accuracy: 0.9521\n",
      "Epoch 14/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3061 - accuracy: 0.9309 - val_loss: 0.2932 - val_accuracy: 0.9500\n",
      "Epoch 15/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2154 - accuracy: 0.9329 - val_loss: 0.2775 - val_accuracy: 0.9564\n",
      "Epoch 16/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1776 - accuracy: 0.9392 - val_loss: 0.2913 - val_accuracy: 0.9557\n",
      "Epoch 17/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2143 - accuracy: 0.9422 - val_loss: 0.2608 - val_accuracy: 0.9599\n",
      "Epoch 18/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1583 - accuracy: 0.9513 - val_loss: 0.2574 - val_accuracy: 0.9564\n",
      "Epoch 19/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1496 - accuracy: 0.9538 - val_loss: 0.2735 - val_accuracy: 0.9536\n",
      "Epoch 20/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1379 - accuracy: 0.9563 - val_loss: 0.2597 - val_accuracy: 0.9627\n",
      "Epoch 21/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1411 - accuracy: 0.9560 - val_loss: 0.2767 - val_accuracy: 0.9620\n",
      "Epoch 22/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1227 - accuracy: 0.9574 - val_loss: 0.2660 - val_accuracy: 0.9662\n",
      "Epoch 23/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1261 - accuracy: 0.9601 - val_loss: 0.2657 - val_accuracy: 0.9669\n",
      "Epoch 24/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1202 - accuracy: 0.9598 - val_loss: 0.2845 - val_accuracy: 0.9578\n",
      "Epoch 25/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1191 - accuracy: 0.9620 - val_loss: 0.2782 - val_accuracy: 0.9613\n",
      "Epoch 26/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1191 - accuracy: 0.9601 - val_loss: 0.2820 - val_accuracy: 0.9641\n",
      "Epoch 27/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1049 - accuracy: 0.9648 - val_loss: 0.2844 - val_accuracy: 0.9634\n",
      "Epoch 28/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0991 - accuracy: 0.9673 - val_loss: 0.2787 - val_accuracy: 0.9627\n",
      "Epoch 29/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1064 - accuracy: 0.9678 - val_loss: 0.2749 - val_accuracy: 0.9634\n",
      "Epoch 30/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0918 - accuracy: 0.9684 - val_loss: 0.2714 - val_accuracy: 0.9669\n",
      "Epoch 31/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0990 - accuracy: 0.9692 - val_loss: 0.2741 - val_accuracy: 0.9648\n",
      "Epoch 32/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0842 - accuracy: 0.9728 - val_loss: 0.2811 - val_accuracy: 0.9669\n",
      "Epoch 33/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0873 - accuracy: 0.9728 - val_loss: 0.2736 - val_accuracy: 0.9669\n",
      "Epoch 34/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0883 - accuracy: 0.9708 - val_loss: 0.2999 - val_accuracy: 0.9634\n",
      "Epoch 35/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0841 - accuracy: 0.9714 - val_loss: 0.2644 - val_accuracy: 0.9655\n",
      "Epoch 36/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0883 - accuracy: 0.9675 - val_loss: 0.2802 - val_accuracy: 0.9641\n",
      "Epoch 37/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0775 - accuracy: 0.9750 - val_loss: 0.2722 - val_accuracy: 0.9690\n",
      "Epoch 38/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0754 - accuracy: 0.9744 - val_loss: 0.2911 - val_accuracy: 0.9641\n",
      "Epoch 39/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0817 - accuracy: 0.9700 - val_loss: 0.2903 - val_accuracy: 0.9641\n",
      "Epoch 40/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0753 - accuracy: 0.9736 - val_loss: 0.3112 - val_accuracy: 0.9634\n",
      "Epoch 41/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0637 - accuracy: 0.9796 - val_loss: 0.2837 - val_accuracy: 0.9662\n",
      "Epoch 42/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0706 - accuracy: 0.9766 - val_loss: 0.2998 - val_accuracy: 0.9690\n",
      "Epoch 43/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0845 - accuracy: 0.9744 - val_loss: 0.3000 - val_accuracy: 0.9648\n",
      "Epoch 44/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0717 - accuracy: 0.9769 - val_loss: 0.3103 - val_accuracy: 0.9676\n",
      "Epoch 45/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0728 - accuracy: 0.9761 - val_loss: 0.3096 - val_accuracy: 0.9704\n",
      "Epoch 46/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0648 - accuracy: 0.9791 - val_loss: 0.3169 - val_accuracy: 0.9655\n",
      "Score for fold 3: loss of 0.1860066056251526; accuracy of 96.58966064453125%\n",
      "————————————————————————————————————\n",
      "Training for fold 4\n",
      "Epoch 1/500\n",
      "114/114 [==============================] - 1s 4ms/step - loss: 3.4076 - accuracy: 0.2465 - val_loss: 2.0097 - val_accuracy: 0.5257\n",
      "Epoch 2/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 1.6349 - accuracy: 0.5367 - val_loss: 1.0293 - val_accuracy: 0.7255\n",
      "Epoch 3/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 1.0916 - accuracy: 0.6781 - val_loss: 0.7273 - val_accuracy: 0.8079\n",
      "Epoch 4/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.8276 - accuracy: 0.7565 - val_loss: 0.5807 - val_accuracy: 0.8431\n",
      "Epoch 5/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.6809 - accuracy: 0.7873 - val_loss: 0.4365 - val_accuracy: 0.8888\n",
      "Epoch 6/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5587 - accuracy: 0.8270 - val_loss: 0.4165 - val_accuracy: 0.8846\n",
      "Epoch 7/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5148 - accuracy: 0.8429 - val_loss: 0.3201 - val_accuracy: 0.9212\n",
      "Epoch 8/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4123 - accuracy: 0.8710 - val_loss: 0.3234 - val_accuracy: 0.9184\n",
      "Epoch 9/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5081 - accuracy: 0.8839 - val_loss: 0.2735 - val_accuracy: 0.9346\n",
      "Epoch 10/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3419 - accuracy: 0.8949 - val_loss: 0.2775 - val_accuracy: 0.9346\n",
      "Epoch 11/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3168 - accuracy: 0.9001 - val_loss: 0.2491 - val_accuracy: 0.9444\n",
      "Epoch 12/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2785 - accuracy: 0.9153 - val_loss: 0.2387 - val_accuracy: 0.9430\n",
      "Epoch 13/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2590 - accuracy: 0.9117 - val_loss: 0.2267 - val_accuracy: 0.9367\n",
      "Epoch 14/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2447 - accuracy: 0.9186 - val_loss: 0.2269 - val_accuracy: 0.9465\n",
      "Epoch 15/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2278 - accuracy: 0.9238 - val_loss: 0.2253 - val_accuracy: 0.9430\n",
      "Epoch 16/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2369 - accuracy: 0.9323 - val_loss: 0.2281 - val_accuracy: 0.9451\n",
      "Epoch 17/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2239 - accuracy: 0.9315 - val_loss: 0.2052 - val_accuracy: 0.9451\n",
      "Epoch 18/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1912 - accuracy: 0.9378 - val_loss: 0.2118 - val_accuracy: 0.9472\n",
      "Epoch 19/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1635 - accuracy: 0.9472 - val_loss: 0.2080 - val_accuracy: 0.9536\n",
      "Epoch 20/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1782 - accuracy: 0.9450 - val_loss: 0.2078 - val_accuracy: 0.9550\n",
      "Epoch 21/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1664 - accuracy: 0.9455 - val_loss: 0.2028 - val_accuracy: 0.9550\n",
      "Epoch 22/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1463 - accuracy: 0.9560 - val_loss: 0.2082 - val_accuracy: 0.9578\n",
      "Epoch 23/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1472 - accuracy: 0.9494 - val_loss: 0.2110 - val_accuracy: 0.9550\n",
      "Epoch 24/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1272 - accuracy: 0.9574 - val_loss: 0.2015 - val_accuracy: 0.9599\n",
      "Epoch 25/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1262 - accuracy: 0.9549 - val_loss: 0.2080 - val_accuracy: 0.9564\n",
      "Epoch 26/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1313 - accuracy: 0.9541 - val_loss: 0.2020 - val_accuracy: 0.9550\n",
      "Epoch 27/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1510 - accuracy: 0.9494 - val_loss: 0.1993 - val_accuracy: 0.9550\n",
      "Epoch 28/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1447 - accuracy: 0.9530 - val_loss: 0.1992 - val_accuracy: 0.9564\n",
      "Epoch 29/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1159 - accuracy: 0.9620 - val_loss: 0.1783 - val_accuracy: 0.9648\n",
      "Epoch 30/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1104 - accuracy: 0.9640 - val_loss: 0.1868 - val_accuracy: 0.9606\n",
      "Epoch 31/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1042 - accuracy: 0.9648 - val_loss: 0.1936 - val_accuracy: 0.9648\n",
      "Epoch 32/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0949 - accuracy: 0.9675 - val_loss: 0.1937 - val_accuracy: 0.9585\n",
      "Epoch 33/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0963 - accuracy: 0.9689 - val_loss: 0.2061 - val_accuracy: 0.9557\n",
      "Epoch 34/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2487 - accuracy: 0.9585 - val_loss: 0.2370 - val_accuracy: 0.9437\n",
      "Epoch 35/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1011 - accuracy: 0.9700 - val_loss: 0.2003 - val_accuracy: 0.9620\n",
      "Epoch 36/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0875 - accuracy: 0.9714 - val_loss: 0.2048 - val_accuracy: 0.9557\n",
      "Epoch 37/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1104 - accuracy: 0.9629 - val_loss: 0.2006 - val_accuracy: 0.9648\n",
      "Epoch 38/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0991 - accuracy: 0.9673 - val_loss: 0.2006 - val_accuracy: 0.9634\n",
      "Epoch 39/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0929 - accuracy: 0.9678 - val_loss: 0.1924 - val_accuracy: 0.9592\n",
      "Epoch 40/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0910 - accuracy: 0.9700 - val_loss: 0.1931 - val_accuracy: 0.9641\n",
      "Epoch 41/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0897 - accuracy: 0.9711 - val_loss: 0.2073 - val_accuracy: 0.9578\n",
      "Score for fold 4: loss of 0.2317051887512207; accuracy of 95.04950642585754%\n",
      "————————————————————————————————————\n",
      "Training for fold 5\n",
      "Epoch 1/500\n",
      "114/114 [==============================] - 1s 4ms/step - loss: 3.1279 - accuracy: 0.2888 - val_loss: 1.6636 - val_accuracy: 0.5658\n",
      "Epoch 2/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 1.3826 - accuracy: 0.6084 - val_loss: 0.8150 - val_accuracy: 0.7966\n",
      "Epoch 3/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.9543 - accuracy: 0.7228 - val_loss: 0.6371 - val_accuracy: 0.8410\n",
      "Epoch 4/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.7185 - accuracy: 0.7888 - val_loss: 0.4331 - val_accuracy: 0.8867\n",
      "Epoch 5/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.5800 - accuracy: 0.8215 - val_loss: 0.3926 - val_accuracy: 0.9001\n",
      "Epoch 6/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4702 - accuracy: 0.8545 - val_loss: 0.3476 - val_accuracy: 0.9247\n",
      "Epoch 7/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4145 - accuracy: 0.8705 - val_loss: 0.2892 - val_accuracy: 0.9388\n",
      "Epoch 8/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.4050 - accuracy: 0.8779 - val_loss: 0.2745 - val_accuracy: 0.9451\n",
      "Epoch 9/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3475 - accuracy: 0.8969 - val_loss: 0.2593 - val_accuracy: 0.9486\n",
      "Epoch 10/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.3047 - accuracy: 0.9048 - val_loss: 0.2582 - val_accuracy: 0.9430\n",
      "Epoch 11/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2527 - accuracy: 0.9263 - val_loss: 0.2464 - val_accuracy: 0.9479\n",
      "Epoch 12/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2529 - accuracy: 0.9249 - val_loss: 0.2412 - val_accuracy: 0.9479\n",
      "Epoch 13/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2403 - accuracy: 0.9307 - val_loss: 0.2238 - val_accuracy: 0.9564\n",
      "Epoch 14/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.2186 - accuracy: 0.9323 - val_loss: 0.2106 - val_accuracy: 0.9592\n",
      "Epoch 15/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1876 - accuracy: 0.9395 - val_loss: 0.2215 - val_accuracy: 0.9599\n",
      "Epoch 16/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1969 - accuracy: 0.9315 - val_loss: 0.2145 - val_accuracy: 0.9564\n",
      "Epoch 17/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1732 - accuracy: 0.9455 - val_loss: 0.2052 - val_accuracy: 0.9592\n",
      "Epoch 18/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1618 - accuracy: 0.9486 - val_loss: 0.1997 - val_accuracy: 0.9634\n",
      "Epoch 19/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1566 - accuracy: 0.9521 - val_loss: 0.2072 - val_accuracy: 0.9592\n",
      "Epoch 20/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1392 - accuracy: 0.9505 - val_loss: 0.1996 - val_accuracy: 0.9599\n",
      "Epoch 21/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1496 - accuracy: 0.9499 - val_loss: 0.2163 - val_accuracy: 0.9599\n",
      "Epoch 22/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1289 - accuracy: 0.9568 - val_loss: 0.2345 - val_accuracy: 0.9557\n",
      "Epoch 23/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1376 - accuracy: 0.9565 - val_loss: 0.2150 - val_accuracy: 0.9599\n",
      "Epoch 24/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1213 - accuracy: 0.9585 - val_loss: 0.2064 - val_accuracy: 0.9641\n",
      "Epoch 25/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1759 - accuracy: 0.9455 - val_loss: 0.2243 - val_accuracy: 0.9641\n",
      "Epoch 26/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1324 - accuracy: 0.9538 - val_loss: 0.2049 - val_accuracy: 0.9669\n",
      "Epoch 27/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1197 - accuracy: 0.9620 - val_loss: 0.2019 - val_accuracy: 0.9655\n",
      "Epoch 28/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1063 - accuracy: 0.9670 - val_loss: 0.2014 - val_accuracy: 0.9662\n",
      "Epoch 29/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1048 - accuracy: 0.9681 - val_loss: 0.2266 - val_accuracy: 0.9662\n",
      "Epoch 30/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1119 - accuracy: 0.9637 - val_loss: 0.2215 - val_accuracy: 0.9599\n",
      "Epoch 31/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1053 - accuracy: 0.9640 - val_loss: 0.2104 - val_accuracy: 0.9627\n",
      "Epoch 32/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0947 - accuracy: 0.9664 - val_loss: 0.2061 - val_accuracy: 0.9655\n",
      "Epoch 33/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0922 - accuracy: 0.9662 - val_loss: 0.2009 - val_accuracy: 0.9704\n",
      "Epoch 34/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0927 - accuracy: 0.9711 - val_loss: 0.2131 - val_accuracy: 0.9683\n",
      "Epoch 35/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0911 - accuracy: 0.9686 - val_loss: 0.2159 - val_accuracy: 0.9655\n",
      "Epoch 36/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.1039 - accuracy: 0.9656 - val_loss: 0.2437 - val_accuracy: 0.9578\n",
      "Epoch 37/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0898 - accuracy: 0.9681 - val_loss: 0.2077 - val_accuracy: 0.9711\n",
      "Epoch 38/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0878 - accuracy: 0.9719 - val_loss: 0.2114 - val_accuracy: 0.9676\n",
      "Epoch 39/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0901 - accuracy: 0.9708 - val_loss: 0.2079 - val_accuracy: 0.9711\n",
      "Epoch 40/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0896 - accuracy: 0.9719 - val_loss: 0.2144 - val_accuracy: 0.9690\n",
      "Epoch 41/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0711 - accuracy: 0.9752 - val_loss: 0.2213 - val_accuracy: 0.9683\n",
      "Epoch 42/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0729 - accuracy: 0.9769 - val_loss: 0.2123 - val_accuracy: 0.9662\n",
      "Epoch 43/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0826 - accuracy: 0.9744 - val_loss: 0.2309 - val_accuracy: 0.9662\n",
      "Epoch 44/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0767 - accuracy: 0.9752 - val_loss: 0.2204 - val_accuracy: 0.9690\n",
      "Epoch 45/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0754 - accuracy: 0.9747 - val_loss: 0.2175 - val_accuracy: 0.9662\n",
      "Epoch 46/500\n",
      "114/114 [==============================] - 0s 3ms/step - loss: 0.0766 - accuracy: 0.9763 - val_loss: 0.2358 - val_accuracy: 0.9655\n",
      "Score for fold 5: loss of 0.21954016387462616; accuracy of 95.37444710731506%\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "#K-fold cross validation\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "kfold = KFold(n_splits=5, shuffle=True)\n",
    "fold_no = 1\n",
    "inputs = np.concatenate((X_train, X_valid), axis=0)\n",
    "targets = np.concatenate((y_train, y_valid), axis=0)\n",
    "opt = tf.keras.optimizers.legacy.Adam(learning_rate=1e-3)\n",
    "acc_per_fold=[]\n",
    "loss_per_fold=[]\n",
    "\n",
    "for train, test in kfold.split(X_train, y_train):\n",
    "    \n",
    "  model_k = Sequential()\n",
    "  model_k.add(Conv1D(16, 3, activation=\"relu\", input_shape=(input_shape, 1), padding='same'))\n",
    "  model_k.add(MaxPooling1D(pool_size=2))\n",
    "  model_k.add(Conv1D(32, 3, activation='relu'))\n",
    "  model_k.add(MaxPooling1D(pool_size=2))\n",
    "  model_k.add(Flatten())\n",
    "  model_k.add(Dense(100, activation='relu'))\n",
    "  model_k.add(Dropout(0.2))\n",
    "  model_k.add(Dense(n_class, activation='softmax'))\n",
    "  #compile the model\n",
    "  model_k.compile(loss=\"categorical_crossentropy\", optimizer=opt, metrics=['accuracy'])\n",
    "\n",
    "  # Generate a print\n",
    "  print('————————————————————————————————————')\n",
    "  print('Training for fold {}'.format(fold_no))\n",
    "\n",
    "  # Fit data to model\n",
    "  H = model_k.fit(inputs[train], targets[train],\n",
    "              batch_size=32,\n",
    "              epochs=500,\n",
    "              verbose=1,\n",
    "              callbacks=[es_k, checkpoint_k],\n",
    "              validation_data=(X_valid, y_valid))\n",
    "\n",
    "  # Generate generalization metrics\n",
    "  scores = model_k.evaluate(inputs[test], targets[test], verbose=0)\n",
    "  print('Score for fold {}: {} of {}; {} of {}%'.format(fold_no, model_k.metrics_names[0], scores[0],\n",
    "                                                        model_k.metrics_names[1], scores[1]*100))\n",
    "  acc_per_fold.append(scores[1] * 100)\n",
    "  loss_per_fold.append(scores[0])\n",
    "\n",
    "  # Increase fold number\n",
    "  fold_no = fold_no + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "acc_per_fold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mean_acc = np.array(acc_per_fold).mean()\n",
    "mean_loss = np.array(loss_per_fold).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plot accuracy\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(range(1, len(acc_per_fold) + 1), acc_per_fold, marker='o')\n",
    "plt.axhline(mean_acc, color='red', linestyle='--', label=f'Mean: {mean_acc:.2f}')\n",
    "plt.title('Accuracy for Each Fold - 90 Subj')\n",
    "plt.xlabel('Fold')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "# Plot loss\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(range(1, len(loss_per_fold) + 1), loss_per_fold, marker='o')\n",
    "plt.axhline(mean_loss, color='red', linestyle='--', label=f'Mean: {mean_loss:.2f}')\n",
    "plt.title('Loss for Each Fold - 90 Subj')\n",
    "plt.xlabel('Fold')\n",
    "plt.ylabel('Loss')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "3q0by9DYVewP"
   },
   "outputs": [],
   "source": [
    "#load model\n",
    "# model = tf.keras.models.load_model('best_weight_mimic-wob-do02-bs32-kf5.h5')\n",
    "# evaluate the network\n",
    "print(\"[INFO] evaluating network...\")\n",
    "predictions = model.predict(X_test, batch_size=32)\n",
    "print(X_test.shape)\n",
    "print(predictions.shape)\n",
    "print(y_test.shape)\n",
    "target_names=list(map(str,lb.classes_))\n",
    "report = classification_report(y_test.argmax(axis=1), predictions.argmax(axis=1), target_names=target_names, digits=4)\n",
    "print(report)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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